NAMIC Wiki - User contributions [en]

Projects:VentricleSegmentation

2014-06-05T01:57:25Z

Zhulj: /* Publication */

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= Ventricles Segmentation =
Extracting the myocardial wall of the left (LV) and right (RV) ventricles are important steps in the diagnosis of cardiac diseases. In this paper, we we propose an method for automatically extracting the ventricles from cardiac CT images, which integrates region growing with shape segmentation in a natural way. In this framework, the shape segmentation provides seed regions for region growing while the latter reconstructs a heart surface for shape decomposition.

= Description =
In the method, the left and right ventricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting
the epicardial surface. To this end, the endocardial surfaces are localized using their geometric features obtained on-line from a CT image. After that, a variational region-growing model is employed to extract the epicaridal surfaces of the ventricles. In particular, the location of the endocardial surface of the left ventricle is determined using an active contour model on the blood-pool surface constructed via thresholding. To localize the right ventricle, the active contour model is performed on a heart
surface extracted based on the left ventricle segmentation result.

*[[File:FlowChartLRV.png|600px]]
Flowchart of the ventricles segmentation framework.

= Results =
The proposed method has been tested using 30 human and 12 pig cardiac CT images. Examples of segmentation for human and pig data are shown below.

*[[File:LRVWallShapeVar.png|600px]]
Myocardium segmentation results of human data with significantly different heart shapes.

*[[File:LRVWallVolVar.png|600px]]
Myocardium segmentation results of pig data with different volume coverages.

= Key Investigators =
*Georgia Tech: Liangjia Zhu and Anthony Yezzi
*BWH: Yi Gao
*Boston University: Allen Tannenbaum

= Publication =
L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), vol 60, no, 10, 2013.

Algorithm:BU

2014-06-05T01:56:46Z

Zhulj: /* Ventricles Segmentation for Diagnosis of Cardiac Diseases */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
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= Overview of SUNY Stony Brook Algorithms (PI: Allen Tannenbaum) =

At the State University of New York at Stony Brook, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Stony Brook University Projects =

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== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==

In this project, we provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data
has much lower resolution than histological image data, but it includes the entire liver
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)
in diﬀusion MRI correlates with the ﬁbrosis stage. [[Projects:LiverFibrosisStaging|More...]]

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== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]

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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==

Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension
to the principal axis transformation method for ﬁnding robust rigid transformation of two
volumes. The additional elastic registration is based on a volume registration method
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]

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== [[Projects:SobolevTracker|Object Tracking With Adaptive Sobolev Active Contours]] ==

In this project we propose adaptive tracking mechanism which can be used in medical video applications, or 3D volume segmentation. The proposed Sobolev active contour model overcomes the
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and
low contrast objects. Experimental results show the advantages of our approach compared
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]

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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==

We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured. [[Projects:MultiScaleShapeSegmentation|More...]]

<font color="red">'''New: '''</font> Gao, Y. and Corn, B. and Schifter, D. and Tannenbaum, A. Multiscale 3D Shape Representation and Segmentation with Applications to Hippocampal/Caudate Extraction from Brain MRI, Medical Image Analysis. 16(2) pp374, 2012

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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

<font color="red">'''New: '''</font> Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.

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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.

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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.

<font color="red">'''New: '''</font> Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .

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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==

The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Image Processing, vol. 22, no. 12, 2013.

<font color="red">'''New: '''</font>L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]

<font color="red">'''New: '''</font> Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.

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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]

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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering, vol. 60, no. 10, 2013.
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.

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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

<font color="red">'''New: '''</font> Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation. Neuroimage, volume 45, 2009, pp. 123-132.

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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.

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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)

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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==

An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]

<font color="red">'''New: '''</font> I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.

<font color="red">'''New: '''</font> P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.

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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==

A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]

<font color="red">'''New: '''</font> I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.

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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.

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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115.

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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.

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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.

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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

<font color="red">'''New: '''</font> X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.

<font color="red">'''New: '''</font> Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.

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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.

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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==

The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.

<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.

<font color="red">'''New: '''</font> Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.

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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

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Algorithm:BU

2014-06-05T01:55:21Z

Zhulj: /* Left Atrium Segmentation for Atrial Fibrillation Treatment */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of SUNY Stony Brook Algorithms (PI: Allen Tannenbaum) =

At the State University of New York at Stony Brook, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Stony Brook University Projects =

{| cellpadding="10" style="text-align:left;"

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== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==

In this project, we provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data
has much lower resolution than histological image data, but it includes the entire liver
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)
in diﬀusion MRI correlates with the ﬁbrosis stage. [[Projects:LiverFibrosisStaging|More...]]

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== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]

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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==

Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension
to the principal axis transformation method for ﬁnding robust rigid transformation of two
volumes. The additional elastic registration is based on a volume registration method
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]

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== [[Projects:SobolevTracker|Object Tracking With Adaptive Sobolev Active Contours]] ==

In this project we propose adaptive tracking mechanism which can be used in medical video applications, or 3D volume segmentation. The proposed Sobolev active contour model overcomes the
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and
low contrast objects. Experimental results show the advantages of our approach compared
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]

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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==

We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured. [[Projects:MultiScaleShapeSegmentation|More...]]

<font color="red">'''New: '''</font> Gao, Y. and Corn, B. and Schifter, D. and Tannenbaum, A. Multiscale 3D Shape Representation and Segmentation with Applications to Hippocampal/Caudate Extraction from Brain MRI, Medical Image Analysis. 16(2) pp374, 2012

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| | [[Image:3D_Segmentation_LA.png|200px]]
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

<font color="red">'''New: '''</font> Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.

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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.

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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.

<font color="red">'''New: '''</font> Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .

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| | [[File:LASegAxialView.png|200px]]
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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==

The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Image Processing, vol. 22, no. 12, 2013.

<font color="red">'''New: '''</font>L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]

<font color="red">'''New: '''</font> Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.

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| | [[Image:LongitudinalAFib.png|200px]]
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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]

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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), to appear.
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| | [[Image:RiskMassSeg.png|200px]]
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.

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| | [[Image:GT-DWI-Reorientation-1.jpg|300px]]
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

<font color="red">'''New: '''</font> Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation. Neuroimage, volume 45, 2009, pp. 123-132.

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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.

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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)

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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==

An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]

<font color="red">'''New: '''</font> I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.

<font color="red">'''New: '''</font> P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.

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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==

A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]

<font color="red">'''New: '''</font> I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.

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| | [[Image:Model3D_upTrans.png|200px]]
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.

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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115.

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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.

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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.

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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

<font color="red">'''New: '''</font> X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.

<font color="red">'''New: '''</font> Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.

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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.

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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==

The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.

<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.

<font color="red">'''New: '''</font> Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.

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| | [[Image:Gatech caudateBands.PNG|200px]]
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

2014 Project Week:GrowCutLevelSetLA

2014-01-10T16:33:14Z

Zhulj: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2014.png|[[2014_Winter_Project_Week#Projects|Projects List]]
Image:AFib_GrowCut.png
</gallery>

==Key Investigators==
* Liangjia Zhu, Ivan Kolesov, Allen Tannenbaum, Stony Brook University
* Yi Gao, University of Alabama Birmingham

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* We have created an editor extension for quickly initializing, interactively segmenting targets. We will improve this module for the LA segmentation.

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Create a loadable module from an existing command line module
* Discuss with Josh about similar work. CARMA has also worked on this using Graph Cuts method.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Demoed and discussed with collaborators and start improving it based on these feedbacks
* Working on its extension to support multi-label segmentation
</div>
</div>

== References ==
* [http://www.na-mic.org/Wiki/index.php/DBP3:Utah Utah DBP]

2014 Project Week:GrowCutLevelSetLA

2014-01-10T16:23:05Z

Zhulj: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2014.png|[[2014_Winter_Project_Week#Projects|Projects List]]
Image:AFib_GrowCut.png
</gallery>

==Key Investigators==
* Liangjia Zhu, Ivan Kolesov, Allen Tannenbaum, Stony Brook University
* Yi Gao, University of Alabama Birmingham

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* We have created an editor extension for quickly initializing, interactively segmenting targets. We will improve this module for the LA segmentation.

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Create a loadable module from an existing command line module
* Discuss with Josh about similar work. CARMA has also worked on this using Graph Cuts method.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Demoed and discussed with collaborators and start improving it based on these feedbacks
* Will extend it to support multi-label segmentation
</div>
</div>

== References ==
* [http://www.na-mic.org/Wiki/index.php/DBP3:Utah Utah DBP]

2014 Project Week:GrowCutLevelSetLA

2014-01-10T16:22:14Z

Zhulj: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2014.png|[[2014_Winter_Project_Week#Projects|Projects List]]
Image:AFib_GrowCut.png
</gallery>

==Key Investigators==
* Liangjia Zhu, Ivan Kolesov, Allen Tannenbaum, Stony Brook University
* Yi Gao, University of Alabama Birmingham

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* We have created an editor extension for quickly initializing, interactively segmenting targets. We will improve this module for the LA segmentation.

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Create a loadable module from an existing command line module
* Discuss with Josh about similar work. CARMA has also worked on this using Graph Cuts method.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Demoed and discussed with collaborators and start improving it based on these feedbacks
* Will extend to support multi-label segmentation
</div>
</div>

== References ==
* [http://www.na-mic.org/Wiki/index.php/DBP3:Utah Utah DBP]

2014 Project Week:GrowCutLevelSetLA

2014-01-10T15:24:18Z

Zhulj: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2014.png|[[2014_Winter_Project_Week#Projects|Projects List]]
Image:AFib_GrowCut.png
</gallery>

==Key Investigators==
* Liangjia Zhu, Ivan Kolesov, Allen Tannenbaum, Stony Brook University
* Yi Gao, University of Alabama Birmingham

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* We have created an editor extension for quickly initializing, interactively segmenting targets. We will improve this module for the LA segmentation.

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Create a loadable module from an existing command line module
* Discuss with Josh about similar work. CARMA has also worked on this using Graph Cuts method.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Demoed and discussed with collaborators and start improving it based on these feedbacks
</div>
</div>

== References ==
* [http://www.na-mic.org/Wiki/index.php/DBP3:Utah Utah DBP]

2014 Project Week:GrowCutLevelSetLA

2014-01-04T06:10:10Z

Zhulj: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2014.png|[[2014_Winter_Project_Week#Projects|Projects List]]
Image:AFib_GrowCut.png
</gallery>

==Key Investigators==
* Liangjia Zhu, Ivan Kolesov, Allen Tannenbaum, Stony Brook University
* Yi Gao, University of Alabama Birmingham

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* We have created an editor extension for quickly initializing, interactively segmenting targets. We will improve this module for the LA segmentation.

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Create a loadable module from an existing command line module
* Discuss with Josh about similar work. CARMA has also worked on this using Graph Cuts method.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
*
</div>
</div>

== References ==
* [http://www.na-mic.org/Wiki/index.php/DBP3:Utah Utah DBP]

2014 Project Week:GrowCutLevelSetLA

2014-01-04T06:09:42Z

Zhulj: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2014.png|[[2014_Winter_Project_Week#Projects|Projects List]]
Image:AFib_GrowCut.png
</gallery>

==Key Investigators==
* Liangjia Zhu, Ivan Kolesov, Allen Tannenbaum, Stony Brook University
* Yi Gao, University of Alabama Birmingham

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* We have created an editor extension for quickly initializing, interactively segmenting targets. We will improve this module for the LA segmentation.

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Create a loadable module from an existing command line module
* Discuss with Josh about similar work. CARMA has also work on this using Graph Cuts method.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
*
</div>
</div>

== References ==
* [http://www.na-mic.org/Wiki/index.php/DBP3:Utah Utah DBP]

2014 Project Week:GrowCutLevelSetLA

2014-01-04T06:07:00Z

Zhulj: /* Key Investigators */

2014 Project Week:GrowCutLevelSetLA

2014-01-04T06:04:17Z

Zhulj: /* Key Investigators */

2014 Project Week:GrowCutLevelSetLA

2014-01-04T06:02:49Z

Zhulj:

2014 Project Week:GrowCutLevelSetLA

2014-01-04T06:01:10Z

Zhulj:

__NOTOC__
<gallery>
Image:PW-SLC2014.png|[[2014_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* Liangjia Zhu, Ivan Kolesov, Allen Tannenbaum, Stony Brook University
* Yi Gao, University of Alabama Birmingham

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
*
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
*
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
*
</div>
</div>

File:AFib GrowCut.png

2014-01-04T05:59:13Z

Zhulj:

2014 Winter Project Week

2014-01-04T05:48:46Z

Zhulj: /* Atrial Fibrillation */

Back to [[Project Events]], [[AHM_2014]], [[Events]]

__NOTOC__
[[image:PW-SLC2014.png|300px]]

Project Week is a hands on activity -- programming using the open source [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the NA-MIC, NCIGT, and NAC calendars. It is held in the summer at MIT, typically the last week of June, and a shorter version is held in Salt Lake City in the winter, typically the second week of January.

Active preparation begins 6-8 weeks prior to the meeting, when a kick-off teleconference is hosted by the NA-MIC Engineering, Dissemination, and Leadership teams, the primary hosts of this event. Invitations to this call are sent to all NA-MIC members, past attendees of the event, as well as any parties who have expressed an interest in working with NA-MIC. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient NA-MIC coverage for all. Subsequent teleconferences allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams are asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself starts off with a short presentation by each project team, driven using their previously created description, and allows all participants to be acquainted with others who are doing similar work. In the rest of the week, about half the time is spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half is spent in project teams, doing hands-on programming, algorithm design, or clinical application of NA-MIC kit tools. The hands-on activities are done in 10-20 small teams of size 3-5, each with a mix of experts in NA-MIC kit software, algorithms, and clinical. To facilitate this work, a large room is setup with several tables, with internet and power access, and each team gathers on a table with their individual laptops, connects to the internet to download their software and data, and is able to work on their projects. On the last day of the event, a closing presentation session is held in which each project team presents a summary of what they accomplished during the week.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].
= Dates.Venue.Registration =

Please [[AHM_2014#Dates_Venue_Registration|click here for Dates, Venue, and Registration]] for this event.

= [[AHM_2014#Agenda|'''AGENDA''']] and Project List=

Please:
* [[AHM_2014#Agenda|'''Click here for the agenda for AHM 2014 and Project Week''']].
* [[#Projects|'''Click here to jump to Project list''']]

=Background and Preparation=

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]

=Projects=
* [[2014_Project_Week_Template | Template for project pages]]

==TBI==
*[[2014_Project_Week:TBIatrophy|Multimodal neuroimaging for the quantification of brain atrophy at six months following severe traumatic brain injury]] (Andrei Irimia, SY Matthew Goh, Carinna M. Torgerson, John D. Van Horn)
*[[2014_Project_Week:TBIdemyelination|Systematic evaluation of axonal demyelination subsequent to traumatic brain injury using structural T1- and T2-weighted magnetic resonance imaging]] (Andrei Irimia, SY Matthew Goh, Carinna M. Torgerson, John D. Van Horn)
*[[2014_Project_Week:BrainAging|Mapping the effect of traumatic brain injury upon white matter connections in the human brain using 3D Slicer]] (Andrei Irimia, John D. Van Horn)
*[[2014_Project_Week:LongitudinalDTI|Patient-specific longitudinal DTI analysis in traumatic brain injury]] (Anuja Sharma, Andrei Irimia, Bo Wang, John D. Van Horn, Martin Styner, Guido gerig)
*[[2014_Project_Week:TBISegmentation|Testing the interactive segmentation algorithm for traumatic brain injury]] (Bo Wang, Marcel Prastawa, Andrei Irimia, John D. Van Horn, Guido Gerig)

==Atrial Fibrillation==
*[[2014_Project_Week:MRAFusionRegistration|DEMRI LA Segmentation via Image Fusion (MRA)]] (Josh, Salma, Alan)
*[[2014_Project_Week:LAFibrosisVisualizationModule|LA Fibrosis / Scar Visualization]] (Josh, Salma, Alan)
*[[2014_Project_Week:CARMADocumentation|CARMA Extension Documentation Project]] (Josh, Salma)
*[[2014_Project_Week:GraphCutsLASegmentationModule|LA Segmentation module using multi-column Graph Cuts]] (Gopal, Salma, Josh, Rob, Ross)
*[[2014_Project_Week:JointImageAndShapeAnalysisForFibrosisDistribution|Joint Image and Shape Analysis for Fibrosis Distribution]](Yi Gao, LiangJia Zhu, Josh Cates, Rob MacLeod, Sylvain Bouix, Ron Kikinis, Allen Tannenbaum)
*[[2014_Project_Week:GrowCutLevelSetLA|Grow cut, level set integration for interactive LA segmentation]] ( Liangjia Zhu, Ivan Kolesov, Yi Gao, Allen Tannenbaum)

==Cardiac==
*[[2014_Project_Week:CardiacStemCellMonitoring|Monitoring engrafted stem cells in cardiac tissue with time series manganese enhanced MRI]] (Karl Diedrich)

==Slicer4 Extensions==
*[[2014_Project_Week:ShapePopulationViewer|Surface Visualization - ShapePopulationViewer]] (Alexis Girault, Francois Budin, Beatriz Panaigua, Martin Styner)

==Huntington's Disease==
*[[2014_Project_Week:DWIDispersion|DWI Dispersion]] (Hans, CF, Peter Savadjiev)
*[[2014_Project_Week:DTIAnalysis|DTI Compressed Sensing?]] (Hans, CF)
*[[2014_Project_Week:Modules scripting|Slicer module scripting?]] (Dave)
*[[2014_Project_Week:DWIConverter|DWIConverter?]] (Hans, Kent)
*[[2014_Project_Week:Slicer_Based_Surface_Template_Estimation|Slicer Based Surface Template Estimation]] (Saurabh JHU, Steve Pieper, Hans Johnson, Josh Cates)
*[[2014_Project_Week:HD_4DShapes|4D shape analysis: application to HD ]] (James Fishbaugh,Hans Johnson, Guido Gerig)
*[[2014_Project_Week:Shape_Registration_and_Regression|Shape registration and regression in Slicer4 ]] (James Fishbaugh,Hans Johnson, Guido Gerig)

==Head and Neck Cancer==
*[[2014_Project_Week:DIR_validation|DIR Validation]] (Nadya and Greg)
*[[2014_Project_Week:Hybrid_bspline|Hybrid B Spline]] (Nadya, Greg, Steve)
*[[2014_Project_Week:CarreraSlice|Interactive Segmentation]] (Ivan, LiangJia, Nadya, Yi, Greg, Allen)

==Stroke==

*[[2014_Project_Week:Multi-Tissue_Stroke_Segmentation|Multi-Tissue Stroke Segmentation]] (Ramesh, Polina B., Polina G.)

==Brain Segmentation==
*[[2014_Project_Week:MultiAtlas_MultiImage_Segmentation|Multi-Atlas based Multi-Image Segmentation]] (Minjeong Kim, Xiaofeng Liu, Jim Miller, Dinggang Shen)

==Image-Guided Interventions==
*[[2014_Project_Week:Ultrasound Visualization and Navigation in Neurosurgery|Ultrasound Visualization and Navigation in Neurosurgery]] (Matthew Toews, Alireza Mehrtash, Csaba Pinter, Andras Lasso, Steve Pieper, William M. Wells III)
*[[2014_Project_Week:OpenIGTLink| OpenIGTLink Interface: New data types and structures]] (Junichi Tokuda, Andras Lasso, Steve Piper, ???)
*[[2014_Project_Week:Statistical Shape Model for robotic spine surgery| Statistical Shape Model for robotic spine surgery]] (Marine Clogenson, ???)

==Radiation Therapy==
*[[2014_Project_Week:DICOM_RT|DICOM RT Export]] (Greg Sharp, Kevin Wang, others??)
*[[2014_Project_Week:DICOM_SRO|DICOM Spatial Registration Export]] (Greg Sharp, Kevin Wang, others??)
*[[2014_Project_Week:Registration_Evaluation|Interactive Registration and Evaluation]] (Kevin Wang, Greg Sharp, others??)
*[[2014_Project_Week:External_Beam_Planning|External Beam Planning Visualization]] (Kevin Wang, Greg Sharp, Csaba Pinter)

==Medical Robotics==
==[http://qiicr.org QIICR]==

*[[2014_Project_Week:4D_NIfTI_Multivolume|4D NIfTI Multivolume Support]] (Jayashree, Andrey, Jim, John)
*[[2014_Project_Week:RT_FormatConversions|RT and ITK Format Conversions]] (Jayashree, Andras, Csaba. John)
*[[2014_Project_Week:BatchConvertDICOM|Python Scripting Slicer DICOM read/write to convert segmentation objects]] (Jayashree, Andrey, Alireza, Steve, Jc, Hans, John)
*[[2014_Project_Week:PkModeling_user_tool|User module for DCE modeling]] (Andrey, Jayashree, Jim, Alireza, Steve, Ron)
*[[2014_Project_Week:DICOM_enhanced_multiframe|DICOM enhanced multiframe object support]] (Andrey, Alireza, David Clunie, Jayashree, Steve, Reinhard, Jim)
*[[2014_Project_Week:Quantitative_Index_Computation|Quantitative Index Computation]] (Ethan Ulrich, Reinhard Beichel, Nicole, Andrey, Jim)
*[[2014_Project_Week:TCIA Browser Extension in Slicer|TCIA Browser Extension in Slicer]] (Alireza, Andrey, Steve, Ron)

==TMJ-OA==
* [[2014_Winter_Project_Week:Constrain Fiducial along Suface|Constrain Fiducial along Suface]] (Vinicius Boen, Nicole Aucoin, Beatriz Paniagua)
* [[2014_Winter_Project_Week:Cropping Multiple Surfaces|Cropping multiple surfaces simultaneously]] (Alexander, Jc, Steve, Vinicius, Beatriz Paniagua)
* [[2014_Winter_Project_Week:Color Code Tables|Color Coded Tables]] (Beatriz Paniagua, Vinicius Boen, Nicole Aucoin, Steve Pieper, Francois Budin)
* [[2014_Winter_Project_Week:4DShape Analysis of mandibular changes|4DShape Analysis of mandibular changes]] (James Fishbaugh, Guido Gerig, Vinicius Boen)

==Chronic Obstructive Pulmonary Disease ==
* [[2014_Winter_Project_Week:CIP Core|Chest Imaging Platform (CIP) - Core Infrastructure]] (Raul San Jose, Rola Harmouche, Pietro Nardelli, James Ross)
* [[2014_Winter_Project_Week:CIP Infrastructure Testing and SuperBuild|CIP Testing and SuperBuild]] (James Ross, Raul San Jose)
* [[2014_Winter_Project_Week:Slicer CIP Slicer MRML| Slicer CIP- MRML consolidation]] (Pietro Nardelli, Rola Harmouche, James Ross, Raul San Jose)
* [[2014_Winter_Project_Week:Slicer CIP Modules| Slicer CIP- Modules]] (Rola Harmouche, Pietro Nardelli, James Ross, Raul San Jose)

==Infrastructure==
*[[2014_Project_Week:MRMLSceneSpeedUp|MRML Scene speed up]] (Jc, Andras Lasso)
*[[2014_Project_Week:MultidimensionalDataSupport|Multidimensional data support]] (Andras Lasso, Andriy Fedorov, Steve Pieper, JC, Kevin Wang)
*CLI - Resources? Conditionals? Autonaming? Provenance? CTK unification? (Jim Miller)
*[[2014_Project_Week:MarkupsModule|Markups Module]] (Nicole Aucoin)
* [[2014_Winter_Project_Week:Steered Registration|Steered Registration]] (Steve, Greg, Kevin, Vinicius, Marcel)
* [[2014_Winter_Project_Week:MRB Extension Dependencies|MRB Extension Dependencies]] (Steve, Jc, Jim, Nicole, Alex)
* [[2014_Winter_Project_Week:SubjectHierarchy|Subject hierarchy]] (Csaba Pinter, Andras Lasso, Steve Pieper, Jc, Jayashree, John, Alireza, Andrey)
* [[2014_Winter_Project_Week:IntegrationOfContourObject|Integration of Contour object]] (Csaba Pinter, Andras Lasso, Steve Pieper, ???)
* [[2014_Winter_Project_Week:NonlinearTransforms|Integration nonlinear transforms]] (Alex Yarmarkovich, Csaba Pinter, Andras Lasso, Steve Pieper, ???)
* [[2014_Winter_Project_Week:Logging|Logging (standardization, logging to file)]] (Nicole Aucoin, Steve Pieper, Jc, Andras Lasso, Csaba Pinter, ???)
* [[2014_Winter_Project_Week:XNATSlicerLink| 3DSlicer annotations in XNAT]] (Erwin Vast, Nicole Aucoin, Andrey Fedorov)
* [[2014_Winter_Project_Week:ParameterSerialization | JSON Parameter Serialization]] (Matt McCormick, ???)

2014 Winter Project Week

2014-01-04T05:48:22Z

Zhulj: /* Atrial Fibrillation */

Back to [[Project Events]], [[AHM_2014]], [[Events]]

__NOTOC__
[[image:PW-SLC2014.png|300px]]

Project Week is a hands on activity -- programming using the open source [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the NA-MIC, NCIGT, and NAC calendars. It is held in the summer at MIT, typically the last week of June, and a shorter version is held in Salt Lake City in the winter, typically the second week of January.

Active preparation begins 6-8 weeks prior to the meeting, when a kick-off teleconference is hosted by the NA-MIC Engineering, Dissemination, and Leadership teams, the primary hosts of this event. Invitations to this call are sent to all NA-MIC members, past attendees of the event, as well as any parties who have expressed an interest in working with NA-MIC. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient NA-MIC coverage for all. Subsequent teleconferences allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams are asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself starts off with a short presentation by each project team, driven using their previously created description, and allows all participants to be acquainted with others who are doing similar work. In the rest of the week, about half the time is spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half is spent in project teams, doing hands-on programming, algorithm design, or clinical application of NA-MIC kit tools. The hands-on activities are done in 10-20 small teams of size 3-5, each with a mix of experts in NA-MIC kit software, algorithms, and clinical. To facilitate this work, a large room is setup with several tables, with internet and power access, and each team gathers on a table with their individual laptops, connects to the internet to download their software and data, and is able to work on their projects. On the last day of the event, a closing presentation session is held in which each project team presents a summary of what they accomplished during the week.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].
= Dates.Venue.Registration =

Please [[AHM_2014#Dates_Venue_Registration|click here for Dates, Venue, and Registration]] for this event.

= [[AHM_2014#Agenda|'''AGENDA''']] and Project List=

Please:
* [[AHM_2014#Agenda|'''Click here for the agenda for AHM 2014 and Project Week''']].
* [[#Projects|'''Click here to jump to Project list''']]

=Background and Preparation=

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]

=Projects=
* [[2014_Project_Week_Template | Template for project pages]]

==TBI==
*[[2014_Project_Week:TBIatrophy|Multimodal neuroimaging for the quantification of brain atrophy at six months following severe traumatic brain injury]] (Andrei Irimia, SY Matthew Goh, Carinna M. Torgerson, John D. Van Horn)
*[[2014_Project_Week:TBIdemyelination|Systematic evaluation of axonal demyelination subsequent to traumatic brain injury using structural T1- and T2-weighted magnetic resonance imaging]] (Andrei Irimia, SY Matthew Goh, Carinna M. Torgerson, John D. Van Horn)
*[[2014_Project_Week:BrainAging|Mapping the effect of traumatic brain injury upon white matter connections in the human brain using 3D Slicer]] (Andrei Irimia, John D. Van Horn)
*[[2014_Project_Week:LongitudinalDTI|Patient-specific longitudinal DTI analysis in traumatic brain injury]] (Anuja Sharma, Andrei Irimia, Bo Wang, John D. Van Horn, Martin Styner, Guido gerig)
*[[2014_Project_Week:TBISegmentation|Testing the interactive segmentation algorithm for traumatic brain injury]] (Bo Wang, Marcel Prastawa, Andrei Irimia, John D. Van Horn, Guido Gerig)

==Atrial Fibrillation==
*[[2014_Project_Week:MRAFusionRegistration|DEMRI LA Segmentation via Image Fusion (MRA)]] (Josh, Salma, Alan)
*[[2014_Project_Week:LAFibrosisVisualizationModule|LA Fibrosis / Scar Visualization]] (Josh, Salma, Alan)
*[[2014_Project_Week:CARMADocumentation|CARMA Extension Documentation Project]] (Josh, Salma)
*[[2014_Project_Week:GraphCutsLASegmentationModule|LA Segmentation module using multi-column Graph Cuts]] (Gopal, Salma, Josh, Rob, Ross)
*[[2014_Project_Week:JointImageAndShapeAnalysisForFibrosisDistribution|Joint Image and Shape Analysis for Fibrosis Distribution]](Yi Gao, LiangJia Zhu, Josh Cates, Rob MacLeod, Sylvain Bouix, Ron Kikinis, Allen Tannenbaum)
*[[2014_Project_Week:GrowCutLevelSetLA|Grow cut, level set integration for interactive LA segmentation]] ( Liangjia Zhu, Ivan Kolesov, Yi Gao, Peter Karasev, Allen Tannenbaum)

==Cardiac==
*[[2014_Project_Week:CardiacStemCellMonitoring|Monitoring engrafted stem cells in cardiac tissue with time series manganese enhanced MRI]] (Karl Diedrich)

==Slicer4 Extensions==
*[[2014_Project_Week:ShapePopulationViewer|Surface Visualization - ShapePopulationViewer]] (Alexis Girault, Francois Budin, Beatriz Panaigua, Martin Styner)

==Huntington's Disease==
*[[2014_Project_Week:DWIDispersion|DWI Dispersion]] (Hans, CF, Peter Savadjiev)
*[[2014_Project_Week:DTIAnalysis|DTI Compressed Sensing?]] (Hans, CF)
*[[2014_Project_Week:Modules scripting|Slicer module scripting?]] (Dave)
*[[2014_Project_Week:DWIConverter|DWIConverter?]] (Hans, Kent)
*[[2014_Project_Week:Slicer_Based_Surface_Template_Estimation|Slicer Based Surface Template Estimation]] (Saurabh JHU, Steve Pieper, Hans Johnson, Josh Cates)
*[[2014_Project_Week:HD_4DShapes|4D shape analysis: application to HD ]] (James Fishbaugh,Hans Johnson, Guido Gerig)
*[[2014_Project_Week:Shape_Registration_and_Regression|Shape registration and regression in Slicer4 ]] (James Fishbaugh,Hans Johnson, Guido Gerig)

==Head and Neck Cancer==
*[[2014_Project_Week:DIR_validation|DIR Validation]] (Nadya and Greg)
*[[2014_Project_Week:Hybrid_bspline|Hybrid B Spline]] (Nadya, Greg, Steve)
*[[2014_Project_Week:CarreraSlice|Interactive Segmentation]] (Ivan, LiangJia, Nadya, Yi, Greg, Allen)

==Stroke==

*[[2014_Project_Week:Multi-Tissue_Stroke_Segmentation|Multi-Tissue Stroke Segmentation]] (Ramesh, Polina B., Polina G.)

==Brain Segmentation==
*[[2014_Project_Week:MultiAtlas_MultiImage_Segmentation|Multi-Atlas based Multi-Image Segmentation]] (Minjeong Kim, Xiaofeng Liu, Jim Miller, Dinggang Shen)

==Image-Guided Interventions==
*[[2014_Project_Week:Ultrasound Visualization and Navigation in Neurosurgery|Ultrasound Visualization and Navigation in Neurosurgery]] (Matthew Toews, Alireza Mehrtash, Csaba Pinter, Andras Lasso, Steve Pieper, William M. Wells III)
*[[2014_Project_Week:OpenIGTLink| OpenIGTLink Interface: New data types and structures]] (Junichi Tokuda, Andras Lasso, Steve Piper, ???)
*[[2014_Project_Week:Statistical Shape Model for robotic spine surgery| Statistical Shape Model for robotic spine surgery]] (Marine Clogenson, ???)

==Radiation Therapy==
*[[2014_Project_Week:DICOM_RT|DICOM RT Export]] (Greg Sharp, Kevin Wang, others??)
*[[2014_Project_Week:DICOM_SRO|DICOM Spatial Registration Export]] (Greg Sharp, Kevin Wang, others??)
*[[2014_Project_Week:Registration_Evaluation|Interactive Registration and Evaluation]] (Kevin Wang, Greg Sharp, others??)
*[[2014_Project_Week:External_Beam_Planning|External Beam Planning Visualization]] (Kevin Wang, Greg Sharp, Csaba Pinter)

==Medical Robotics==
==[http://qiicr.org QIICR]==

*[[2014_Project_Week:4D_NIfTI_Multivolume|4D NIfTI Multivolume Support]] (Jayashree, Andrey, Jim, John)
*[[2014_Project_Week:RT_FormatConversions|RT and ITK Format Conversions]] (Jayashree, Andras, Csaba. John)
*[[2014_Project_Week:BatchConvertDICOM|Python Scripting Slicer DICOM read/write to convert segmentation objects]] (Jayashree, Andrey, Alireza, Steve, Jc, Hans, John)
*[[2014_Project_Week:PkModeling_user_tool|User module for DCE modeling]] (Andrey, Jayashree, Jim, Alireza, Steve, Ron)
*[[2014_Project_Week:DICOM_enhanced_multiframe|DICOM enhanced multiframe object support]] (Andrey, Alireza, David Clunie, Jayashree, Steve, Reinhard, Jim)
*[[2014_Project_Week:Quantitative_Index_Computation|Quantitative Index Computation]] (Ethan Ulrich, Reinhard Beichel, Nicole, Andrey, Jim)
*[[2014_Project_Week:TCIA Browser Extension in Slicer|TCIA Browser Extension in Slicer]] (Alireza, Andrey, Steve, Ron)

==TMJ-OA==
* [[2014_Winter_Project_Week:Constrain Fiducial along Suface|Constrain Fiducial along Suface]] (Vinicius Boen, Nicole Aucoin, Beatriz Paniagua)
* [[2014_Winter_Project_Week:Cropping Multiple Surfaces|Cropping multiple surfaces simultaneously]] (Alexander, Jc, Steve, Vinicius, Beatriz Paniagua)
* [[2014_Winter_Project_Week:Color Code Tables|Color Coded Tables]] (Beatriz Paniagua, Vinicius Boen, Nicole Aucoin, Steve Pieper, Francois Budin)
* [[2014_Winter_Project_Week:4DShape Analysis of mandibular changes|4DShape Analysis of mandibular changes]] (James Fishbaugh, Guido Gerig, Vinicius Boen)

==Chronic Obstructive Pulmonary Disease ==
* [[2014_Winter_Project_Week:CIP Core|Chest Imaging Platform (CIP) - Core Infrastructure]] (Raul San Jose, Rola Harmouche, Pietro Nardelli, James Ross)
* [[2014_Winter_Project_Week:CIP Infrastructure Testing and SuperBuild|CIP Testing and SuperBuild]] (James Ross, Raul San Jose)
* [[2014_Winter_Project_Week:Slicer CIP Slicer MRML| Slicer CIP- MRML consolidation]] (Pietro Nardelli, Rola Harmouche, James Ross, Raul San Jose)
* [[2014_Winter_Project_Week:Slicer CIP Modules| Slicer CIP- Modules]] (Rola Harmouche, Pietro Nardelli, James Ross, Raul San Jose)

==Infrastructure==
*[[2014_Project_Week:MRMLSceneSpeedUp|MRML Scene speed up]] (Jc, Andras Lasso)
*[[2014_Project_Week:MultidimensionalDataSupport|Multidimensional data support]] (Andras Lasso, Andriy Fedorov, Steve Pieper, JC, Kevin Wang)
*CLI - Resources? Conditionals? Autonaming? Provenance? CTK unification? (Jim Miller)
*[[2014_Project_Week:MarkupsModule|Markups Module]] (Nicole Aucoin)
* [[2014_Winter_Project_Week:Steered Registration|Steered Registration]] (Steve, Greg, Kevin, Vinicius, Marcel)
* [[2014_Winter_Project_Week:MRB Extension Dependencies|MRB Extension Dependencies]] (Steve, Jc, Jim, Nicole, Alex)
* [[2014_Winter_Project_Week:SubjectHierarchy|Subject hierarchy]] (Csaba Pinter, Andras Lasso, Steve Pieper, Jc, Jayashree, John, Alireza, Andrey)
* [[2014_Winter_Project_Week:IntegrationOfContourObject|Integration of Contour object]] (Csaba Pinter, Andras Lasso, Steve Pieper, ???)
* [[2014_Winter_Project_Week:NonlinearTransforms|Integration nonlinear transforms]] (Alex Yarmarkovich, Csaba Pinter, Andras Lasso, Steve Pieper, ???)
* [[2014_Winter_Project_Week:Logging|Logging (standardization, logging to file)]] (Nicole Aucoin, Steve Pieper, Jc, Andras Lasso, Csaba Pinter, ???)
* [[2014_Winter_Project_Week:XNATSlicerLink| 3DSlicer annotations in XNAT]] (Erwin Vast, Nicole Aucoin, Andrey Fedorov)
* [[2014_Winter_Project_Week:ParameterSerialization | JSON Parameter Serialization]] (Matt McCormick, ???)

2014 Project Week:GrowCutLevelSetLA

2014-01-04T05:44:34Z

Zhulj: Created page with '==Key Investigators== * Liangjia Zhu, Ivan Kolesov, Allen Tannenbaum, Stony Brook University * Yi Gao, University of Alabama Birmingham'

==Key Investigators==

* Liangjia Zhu, Ivan Kolesov, Allen Tannenbaum, Stony Brook University
* Yi Gao, University of Alabama Birmingham

2014 Winter Project Week

2014-01-04T05:43:17Z

Zhulj: /* Atrial Fibrillation */

Back to [[Project Events]], [[AHM_2014]], [[Events]]

__NOTOC__
[[image:PW-SLC2014.png|300px]]

Project Week is a hands on activity -- programming using the open source [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the NA-MIC, NCIGT, and NAC calendars. It is held in the summer at MIT, typically the last week of June, and a shorter version is held in Salt Lake City in the winter, typically the second week of January.

Active preparation begins 6-8 weeks prior to the meeting, when a kick-off teleconference is hosted by the NA-MIC Engineering, Dissemination, and Leadership teams, the primary hosts of this event. Invitations to this call are sent to all NA-MIC members, past attendees of the event, as well as any parties who have expressed an interest in working with NA-MIC. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient NA-MIC coverage for all. Subsequent teleconferences allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams are asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself starts off with a short presentation by each project team, driven using their previously created description, and allows all participants to be acquainted with others who are doing similar work. In the rest of the week, about half the time is spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half is spent in project teams, doing hands-on programming, algorithm design, or clinical application of NA-MIC kit tools. The hands-on activities are done in 10-20 small teams of size 3-5, each with a mix of experts in NA-MIC kit software, algorithms, and clinical. To facilitate this work, a large room is setup with several tables, with internet and power access, and each team gathers on a table with their individual laptops, connects to the internet to download their software and data, and is able to work on their projects. On the last day of the event, a closing presentation session is held in which each project team presents a summary of what they accomplished during the week.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].
= Dates.Venue.Registration =

Please [[AHM_2014#Dates_Venue_Registration|click here for Dates, Venue, and Registration]] for this event.

= [[AHM_2014#Agenda|'''AGENDA''']] and Project List=

Please:
* [[AHM_2014#Agenda|'''Click here for the agenda for AHM 2014 and Project Week''']].
* [[#Projects|'''Click here to jump to Project list''']]

=Background and Preparation=

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]

=Projects=
* [[2014_Project_Week_Template | Template for project pages]]

==TBI==
*[[2014_Project_Week:TBIatrophy|Multimodal neuroimaging for the quantification of brain atrophy at six months following severe traumatic brain injury]] (Andrei Irimia, SY Matthew Goh, Carinna M. Torgerson, John D. Van Horn)
*[[2014_Project_Week:TBIdemyelination|Systematic evaluation of axonal demyelination subsequent to traumatic brain injury using structural T1- and T2-weighted magnetic resonance imaging]] (Andrei Irimia, SY Matthew Goh, Carinna M. Torgerson, John D. Van Horn)
*[[2014_Project_Week:BrainAging|Mapping the effect of traumatic brain injury upon white matter connections in the human brain using 3D Slicer]] (Andrei Irimia, John D. Van Horn)
*[[2014_Project_Week:LongitudinalDTI|Patient-specific longitudinal DTI analysis in traumatic brain injury]] (Anuja Sharma, Andrei Irimia, Bo Wang, John D. Van Horn, Martin Styner, Guido gerig)
*[[2014_Project_Week:TBISegmentation|Testing the interactive segmentation algorithm for traumatic brain injury]] (Bo Wang, Marcel Prastawa, Andrei Irimia, John D. Van Horn, Guido Gerig)

==Atrial Fibrillation==
*[[2014_Project_Week:MRAFusionRegistration|DEMRI LA Segmentation via Image Fusion (MRA)]] (Josh, Salma, Alan)
*[[2014_Project_Week:LAFibrosisVisualizationModule|LA Fibrosis / Scar Visualization]] (Josh, Salma, Alan)
*[[2014_Project_Week:CARMADocumentation|CARMA Extension Documentation Project]] (Josh, Salma)
*[[2014_Project_Week:GraphCutsLASegmentationModule|LA Segmentation module using multi-column Graph Cuts]] (Gopal, Salma, Josh, Rob, Ross)
*[[2014_Project_Week:JointImageAndShapeAnalysisForFibrosisDistribution|Joint Image and Shape Analysis for Fibrosis Distribution]](Yi Gao, LiangJia Zhu, Josh Cates, Rob MacLeod, Sylvain Bouix, Ron Kikinis, Allen Tannenbaum)
*[[2014_Project_Week:GrowCutLevelSetLA|Grow cut, level set integration for interactive LA segmentation]] (Ivan Kolesov, Liangjia Zhu, Yi Gao, Peter Karasev, Allen Tannenbaum)

==Cardiac==
*[[2014_Project_Week:CardiacStemCellMonitoring|Monitoring engrafted stem cells in cardiac tissue with time series manganese enhanced MRI]] (Karl Diedrich)

==Slicer4 Extensions==
*[[2014_Project_Week:ShapePopulationViewer|Surface Visualization - ShapePopulationViewer]] (Alexis Girault, Francois Budin, Beatriz Panaigua, Martin Styner)

==Huntington's Disease==
*[[2014_Project_Week:DWIDispersion|DWI Dispersion]] (Hans, CF, Peter Savadjiev)
*[[2014_Project_Week:DTIAnalysis|DTI Compressed Sensing?]] (Hans, CF)
*[[2014_Project_Week:Modules scripting|Slicer module scripting?]] (Dave)
*[[2014_Project_Week:DWIConverter|DWIConverter?]] (Hans, Kent)
*[[2014_Project_Week:Slicer_Based_Surface_Template_Estimation|Slicer Based Surface Template Estimation]] (Saurabh JHU, Steve Pieper, Hans Johnson, Josh Cates)
*[[2014_Project_Week:HD_4DShapes|4D shape analysis: application to HD ]] (James Fishbaugh,Hans Johnson, Guido Gerig)
*[[2014_Project_Week:Shape_Registration_and_Regression|Shape registration and regression in Slicer4 ]] (James Fishbaugh,Hans Johnson, Guido Gerig)

==Head and Neck Cancer==
*[[2014_Project_Week:DIR_validation|DIR Validation]] (Nadya and Greg)
*[[2014_Project_Week:Hybrid_bspline|Hybrid B Spline]] (Nadya, Greg, Steve)
*[[2014_Project_Week:CarreraSlice|Interactive Segmentation]] (Ivan, LiangJia, Nadya, Yi, Greg, Allen)

==Stroke==

*[[2014_Project_Week:Multi-Tissue_Stroke_Segmentation|Multi-Tissue Stroke Segmentation]] (Ramesh, Polina B., Polina G.)

==Brain Segmentation==
*[[2014_Project_Week:MultiAtlas_MultiImage_Segmentation|Multi-Atlas based Multi-Image Segmentation]] (Minjeong Kim, Xiaofeng Liu, Jim Miller, Dinggang Shen)

==Image-Guided Interventions==
*[[2014_Project_Week:Ultrasound Visualization and Navigation in Neurosurgery|Ultrasound Visualization and Navigation in Neurosurgery]] (Matthew Toews, Alireza Mehrtash, Csaba Pinter, Andras Lasso, Steve Pieper, William M. Wells III)
*[[2014_Project_Week:OpenIGTLink| OpenIGTLink Interface: New data types and structures]] (Junichi Tokuda, Andras Lasso, Steve Piper, ???)
*[[2014_Project_Week:Statistical Shape Model for robotic spine surgery| Statistical Shape Model for robotic spine surgery]] (Marine Clogenson, ???)

==Radiation Therapy==
*[[2014_Project_Week:DICOM_RT|DICOM RT Export]] (Greg Sharp, Kevin Wang, others??)
*[[2014_Project_Week:DICOM_SRO|DICOM Spatial Registration Export]] (Greg Sharp, Kevin Wang, others??)
*[[2014_Project_Week:Registration_Evaluation|Interactive Registration and Evaluation]] (Kevin Wang, Greg Sharp, others??)
*[[2014_Project_Week:External_Beam_Planning|External Beam Planning Visualization]] (Kevin Wang, Greg Sharp, Csaba Pinter)

==Medical Robotics==
==[http://qiicr.org QIICR]==

*[[2014_Project_Week:4D_NIfTI_Multivolume|4D NIfTI Multivolume Support]] (Jayashree, Andrey, Jim, John)
*[[2014_Project_Week:RT_FormatConversions|RT and ITK Format Conversions]] (Jayashree, Andras, Csaba. John)
*[[2014_Project_Week:BatchConvertDICOM|Python Scripting Slicer DICOM read/write to convert segmentation objects]] (Jayashree, Andrey, Alireza, Steve, Jc, Hans, John)
*[[2014_Project_Week:PkModeling_user_tool|User module for DCE modeling]] (Andrey, Jayashree, Jim, Alireza, Steve, Ron)
*[[2014_Project_Week:DICOM_enhanced_multiframe|DICOM enhanced multiframe object support]] (Andrey, Alireza, David Clunie, Jayashree, Steve, Reinhard, Jim)
*[[2014_Project_Week:Quantitative_Index_Computation|Quantitative Index Computation]] (Ethan Ulrich, Reinhard Beichel, Nicole, Andrey, Jim)
*[[2014_Project_Week:TCIA Browser Extension in Slicer|TCIA Browser Extension in Slicer]] (Alireza, Andrey, Steve, Ron)

==TMJ-OA==
* [[2014_Winter_Project_Week:Constrain Fiducial along Suface|Constrain Fiducial along Suface]] (Vinicius Boen, Nicole Aucoin, Beatriz Paniagua)
* [[2014_Winter_Project_Week:Cropping Multiple Surfaces|Cropping multiple surfaces simultaneously]] (Alexander, Jc, Steve, Vinicius, Beatriz Paniagua)
* [[2014_Winter_Project_Week:Color Code Tables|Color Coded Tables]] (Beatriz Paniagua, Vinicius Boen, Nicole Aucoin, Steve Pieper, Francois Budin)
* [[2014_Winter_Project_Week:4DShape Analysis of mandibular changes|4DShape Analysis of mandibular changes]] (James Fishbaugh, Guido Gerig, Vinicius Boen)

==Chronic Obstructive Pulmonary Disease ==
* [[2014_Winter_Project_Week:CIP Core|Chest Imaging Platform (CIP) - Core Infrastructure]] (Raul San Jose, Rola Harmouche, Pietro Nardelli, James Ross)
* [[2014_Winter_Project_Week:CIP Infrastructure Testing and SuperBuild|CIP Testing and SuperBuild]] (James Ross, Raul San Jose)
* [[2014_Winter_Project_Week:Slicer CIP Slicer MRML| Slicer CIP- MRML consolidation]] (Pietro Nardelli, Rola Harmouche, James Ross, Raul San Jose)
* [[2014_Winter_Project_Week:Slicer CIP Modules| Slicer CIP- Modules]] (Rola Harmouche, Pietro Nardelli, James Ross, Raul San Jose)

==Infrastructure==
*[[2014_Project_Week:MRMLSceneSpeedUp|MRML Scene speed up]] (Jc, Andras Lasso)
*[[2014_Project_Week:MultidimensionalDataSupport|Multidimensional data support]] (Andras Lasso, Andriy Fedorov, Steve Pieper, JC, Kevin Wang)
*CLI - Resources? Conditionals? Autonaming? Provenance? CTK unification? (Jim Miller)
*[[2014_Project_Week:MarkupsModule|Markups Module]] (Nicole Aucoin)
* [[2014_Winter_Project_Week:Steered Registration|Steered Registration]] (Steve, Greg, Kevin, Vinicius, Marcel)
* [[2014_Winter_Project_Week:MRB Extension Dependencies|MRB Extension Dependencies]] (Steve, Jc, Jim, Nicole, Alex)
* [[2014_Winter_Project_Week:SubjectHierarchy|Subject hierarchy]] (Csaba Pinter, Andras Lasso, Steve Pieper, Jc, Jayashree, John, Alireza, Andrey)
* [[2014_Winter_Project_Week:IntegrationOfContourObject|Integration of Contour object]] (Csaba Pinter, Andras Lasso, Steve Pieper, ???)
* [[2014_Winter_Project_Week:NonlinearTransforms|Integration nonlinear transforms]] (Alex Yarmarkovich, Csaba Pinter, Andras Lasso, Steve Pieper, ???)
* [[2014_Winter_Project_Week:Logging|Logging (standardization, logging to file)]] (Nicole Aucoin, Steve Pieper, Jc, Andras Lasso, Csaba Pinter, ???)
* [[2014_Winter_Project_Week:XNATSlicerLink| 3DSlicer annotations in XNAT]] (Erwin Vast, Nicole Aucoin, Andrey Fedorov)
* [[2014_Winter_Project_Week:ParameterSerialization | JSON Parameter Serialization]] (Matt McCormick, ???)

DBP Utah Atrial Fibrillation 2014

2013-12-13T17:43:54Z

Zhulj:

[[AHM_2014#Agenda|Back to AHM_2014 Agenda]]

*Time: 2-3pm
*Goal: Get together with your partners from algorithm and engineering to make plans for the upcoming year.
**Create Slicer modules
**Create Slicer extensions
**Create Slicer workflows
*DBP PI: Rob MacLeod
*Algorithms as extensions in Slicer: Ross Whitaker, Allen Tannenbaum
**Left atrial scar segmenter (Liangjia Zhu, Yi Gao, Josh Cates, Alan Morris, Danny Perry, Greg Gardner, Rob MacLeod, Allen Tannenbaum)
**Left atrium segmenter (Liangjia Zhu, Yi Gao, Josh Cates, Alan Morris, Danny Perry, Greg Gardner, Rob MacLeod, Allen Tannenbaum)
**Fibrosis distribution analysis (Yi Gao, Liangjia Zhu, Josh Cates, Alan Morris, Danny Perry, Greg Gardner, Rob MacLeod, Sylvain Bouix, Allen Tannenbaum)
**Grow cut, level set integration for interactive segmentation (Ivan Kolesov, Liangjia Zhu, Yi Gao, Peter Karasev, Allen Tannenbaum)
*Engineering: workflows in collaboration with DBP: Jim Miller

DBP Utah Atrial Fibrillation 2014

2013-12-13T17:43:38Z

Zhulj:

[[AHM_2014#Agenda|Back to AHM_2014 Agenda]]

*Time: 2-3pm
*Goal: Get together with your partners from algorithm and engineering to make plans for the upcoming year.
**Create Slicer modules
**Create Slicer extensions
**Create Slicer workflows
*DBP PI: Rob MacLeod
*Algorithms as extensions in Slicer: Ross Whitaker, Allen Tannenbaum
**Left atrial scar segmenter (Liangjia Zhu, Yi Gao, Josh Cates, Alan Morris, Danny Perry, Greg Gardner, Rob MacLeod, Allen Tannenbaum)
**Left atrium segmenter (Liangjia Zhu, Yi Gao, Josh Cates, Alan Morris, Danny Perry, Greg Gardner, Rob MacLeod, Allen Tannenbaum)
**Fibrosis distribution analysis (Yi Gao, Liangjia Zhu, Josh Cates, Alan Morris, Danny Perry, Greg Gardner, Rob MacLeod, Sylvain Bouix, Allen Tannenbaum)
**Grow cut, level set integration for interactive segmentation (Ivan Kolesov, LiangJia Zhu, Yi Gao, Peter Karasev, Allen Tannenbaum)
*Engineering: workflows in collaboration with DBP: Jim Miller

DBP Utah Atrial Fibrillation 2014

2013-12-13T03:19:59Z

Zhulj:

Algorithm:SlicerModules

2013-09-01T03:14:26Z

Zhulj: /* Algorithms Core: Slicer Modules Under Development */

= Algorithms Core: Slicer Modules Under Development =
These modules will become available as extensions in the Slicer Extension Manager

* Left atrium segmentation: In collaboration with the Afib DBP. Graph-cut based segmentation that loads meshes, allows users to interactively "center" the model, perform the graph-cut segmentation, scan convert results into a volumetric format. Contact: Gopal Veni, University of Utah.

* Multimaterial meshing: Surface and volumetric meshing of multimaterial volumes with output surface triangles viewable within Slicer. Contact: Jonathan Bronson, University of Utah.

* Interactive segmentation: Control based interactive segmentation module, allowing users to use feedback and observer based principles to drive active contours to equilibrium position and capture desired features. Contact: Ivan Kolesov, SUNY Stony Brook.

* Sobolev active contours: Robust implementation of the active contour methodology using a Sobolev norm, giving much better results in the presence of noise. Contact: Arie Nakhmani, UAB.

* Model-based RSS for left atrium segmentation: RSS integrated with a shape prior that is specifically desgined for segmentating the left atrium from MR images. Contact: Liangjia Zhu, SUNY Stony Brook.

* Left atrial scar segmentation: Given the endocardium of the left atrium, this module automatically extracts the scarring tissue. Contact: Liangjia Zhu, SUNY Stony Brook.

Algorithm:SlicerModules

2013-09-01T03:14:00Z

Zhulj: /* Algorithms Core: Slicer Modules Under Development */

= Algorithms Core: Slicer Modules Under Development =
These modules will become available as extensions in the Slicer Extension Manager

* Left atrium segmentation: In collaboration with the Afib DBP. Graph-cut based segmentation that loads meshes, allows users to interactively "center" the model, perform the graph-cut segmentation, scan convert results into a volumetric format. Contact: Gopal Veni, University of Utah.

* Multimaterial meshing: Surface and volumetric meshing of multimaterial volumes with output surface triangles viewable within Slicer. Contact: Jonathan Bronson, University of Utah.

* Interactive segmentation: Control based interactive segmentation module, allowing users to use feedback and observer based principles to drive active contours to equilibrium position and capture desired features. Contact: Ivan Kolesov, SUNY Stony Brook.

* Sobolev active contours: Robust implementation of the active contour methodology using a Sobolev norm, giving much better results in the presence of noise. Contact: Arie Nakhmani, UAB.

* Model-based RSS for left atrium segmentation: RSS integrated with a shape prior that is specifically desgined for segmentating the left atrium from MR images. Contact: Liangjia Zhu, SUNY Stony Brook.

* Left atrial scar segmentation: Given the endocardium of the left atrium, this module automatically extract the scarring tissue. Contact: Liangjia Zhu, SUNY Stony Brook.

Projects:LeftAtriumSegmentation

2013-06-22T02:20:44Z

Zhulj:

Back to [[Algorithm:BU|Boston University Algorithms]]
__NOTOC__
= Automatic Segmentation of Left Atrium via Variational Region Growing=

= Description =
Automatic segmentation of the left atrium (LA) from MRI data is a challenging but major task in medical imaging
analysis. An important application is concerned with the treatment of left atrial fibrillation [1]. Atrial fibrillation is a cardiac
arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart. One of the treatments
for such arrhythmia is the catheter ablation, which targets specific parts of the LA for radio-frequency ablation using
an intracardiac catheter [2]. Application of radio-frequency energy to the cardiac tissue causes thermal injury, which in
turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and
effectively cure atrial fibrillation. In order to perform such ablation, the extraction of the LA from the late gadolinium
enhancement MR (LGE-MR) images is required and is often performed manually, which is a very time-consuming task.
However, automatic LA segmentation is challenging due to the following factors: 1) the LA sized is relatively small as
compared to the left ventricle (LV) or lungs in cardiac MRI images; 2) boundaries are not clearly defined when the blood
pool goes into the pulmonary veins from the LA; 3) the shape variability of LA is large across subjects.

We propose an automatic approach for segmenting the left atrium from magnetic resonance imagery
(MRI). The segmentation problem is formulated as a problem in variational region growing. In particular, the method starts
locally by searching for a seed region of the left atrium from a given MR slice. A global constraint is imposed by applying a
shape prior to the left atrium represented by Zernike moments. The overall growing process is guided by the robust statistics of
intensities from the seed region along with the shape prior to capture the whole atrial region.

The proposed method consists of two key steps:
*(1) search for a seed region of the LA from an image slice in the axial view.

*(2) explore the LA region using a variational region-growing process. A shape prior is employed to drive the growing process towards atrium-like shapes.
Given a properly set seed region, a growing process driven by the robust statistics of the seed region is employed to explore the entire LA region. However, leakage is almost
inevitable because the statistics computed does not provide a global shape constraint on evolving contours. Hence, a shape
prior is applied to attract the growing process towards an expected shape.

= Results =
*[[File:Image-LASegWithMomentsPrior.png|800px]]
Region-growing process driven by robust statistics and Zernike moments shape prior.

*[[File:LASegRG2Atlas.png|800px]]
Comparison of the worst results obtained using the proposed method (first column) and the atlas-based method (second column) . From top to bottom:
the LA returned using the proposed method (red) and atlas-based method (green) in axial, coronal, and sagittal views, respectively.

= Key Investigators =
*Georgia Tech: Liangjia Zhu and Anthony Yezzi
*BWH: Yi Gao
*Boston University: Allen Tannenbaum

= Publications =
L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

= References =
1. C. J. McGann, E. G. Kholmovski, R. S. Oakes, J. E. Blauer, Segerson N. M. Daccarett, M., K. J. Airey, N. Akoum, E. N. Fish, T. J. Badger,
E. V. R. DiBella, D. Parker, R. S. MacLeod, and N. F. Marrouche. New magnetic resonance imaging based method to define extent of left atrial wall injury after the ablation of atrial fibrillation. Journal of the American College of Cardiology, 2008.

2. P. Jais, R. Weerasooriya, D.C. Shah, M. Hocini, L. Macle, K.J. Choi, C. Scavee, M. Ha ̈ssaguerre, and J. Clementy. Ablation therapy for atrial fibrillation (AF). Cardiovascular research, 54(2):337–346, 2002.

Algorithm:BU

2013-06-05T04:08:59Z

Zhulj: /* Ventricles Segmentation for Diagnosis of Cardiac Diseases */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Boston University/UAB Algorithms (PI: Allen Tannenbaum) =

At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Boston University/UAB Projects =

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== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==

In this project, we provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data
has much lower resolution than histological image data, but it includes the entire liver
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)
in diﬀusion MRI correlates with the ﬁbrosis stage. [[Projects:LiverFibrosisStaging|More...]]

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== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]

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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==

Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension
to the principal axis transformation method for ﬁnding robust rigid transformation of two
volumes. The additional elastic registration is based on a volume registration method
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]

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== [[Projects:SobolevTracker|Object Tracking With Adaptive Sobolev Active Contours]] ==

In this project we propose adaptive tracking mechanism which can be used in medical video applications, or 3D volume segmentation. The proposed Sobolev active contour model overcomes the
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and
low contrast objects. Experimental results show the advantages of our approach compared
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]

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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==

We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured. [[Projects:MultiScaleShapeSegmentation|More...]]

<font color="red">'''New: '''</font> Gao, Y. and Corn, B. and Schifter, D. and Tannenbaum, A. Multiscale 3D Shape Representation and Segmentation with Applications to Hippocampal/Caudate Extraction from Brain MRI, Medical Image Analysis. 16(2) pp374, 2012

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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

<font color="red">'''New: '''</font> Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.

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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.

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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.

<font color="red">'''New: '''</font> Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .

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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==

The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), submitted.

<font color="red">'''New: '''</font>L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]

<font color="red">'''New: '''</font> Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.

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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]

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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), to appear.
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.

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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

<font color="red">'''New: '''</font> Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation. Neuroimage, volume 45, 2009, pp. 123-132.

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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.

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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)

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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==

An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]

<font color="red">'''New: '''</font> I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.

<font color="red">'''New: '''</font> P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.

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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==

A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]

<font color="red">'''New: '''</font> I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.

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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.

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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115.

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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.

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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.

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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

<font color="red">'''New: '''</font> X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.

<font color="red">'''New: '''</font> Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.

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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.

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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==

The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.

<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.

<font color="red">'''New: '''</font> Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.

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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

2013 Project Week:CARMA Scar Segmentation

2013-01-10T23:22:33Z

Zhulj:

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
Image:LAScarSeg.png|Scar Segmentation GUI
</gallery>

==Key Investigators==

* UAB: LiangJia Zhu, Allen Tannenbaum
* BWH:Yi Gao
* SCI: Josh Cates, Rob MacLeod
==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* The objective is to develop an effective scheme for identifying scar tissue. We will integrate the intensity distribution from LA chamber into the overall identification process.
* We will discuss possible improvements/extensions to this work.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Develop the scar identification as a CLI module in Slicer
* Test the method with CARMA data.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Delivered scar segmentation as a CLI module
</div>
</div>

2013 Project Week:CARMA Scar Segmentation

2013-01-10T23:21:13Z

Zhulj:

2013 Project Week:CARMA Scar Segmentation

2013-01-10T23:20:55Z

Zhulj:

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
Image:LAScarSeg.png|[Scar Segmentation GUI]
</gallery>

==Key Investigators==

* UAB: LiangJia Zhu, Allen Tannenbaum
* BWH:Yi Gao
* SCI: Josh Cates, Rob MacLeod
==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* The objective is to develop an effective scheme for identifying scar tissue. We will integrate the intensity distribution from LA chamber into the overall identification process.
* We will discuss possible improvements/extensions to this work.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Develop the scar identification as a CLI module in Slicer
* Test the method with CARMA data.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>

</div>
</div>

File:LAScarSeg.png

2013-01-10T23:19:40Z

Zhulj:

2013 Project Week:CARMA Scar Segmentation

2013-01-07T15:28:56Z

Zhulj:

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==

* UAB: LiangJia Zhu, Allen Tannenbaum
* BWH:Yi Gao
* SCI: Josh Cates, Rob MacLeod
==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* The objective is to develop an effective scheme for identifying scar tissue. We will integrate the intensity distribution from LA chamber into the overall identification process.
* We will discuss possible improvements/extensions to this work.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Develop the scar identification as a CLI module in Slicer
* Test the method with CARMA data.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>

</div>
</div>

2013 Project Week Template

2013-01-07T07:09:30Z

Zhulj:

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==

Name, Institute

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
*
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
*
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
*
</div>
</div>

2013 Project Week Template

2013-01-07T07:02:55Z

Zhulj: Undo revision 79309 by Zhulj (Talk)

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
Image:ScarSeg_EM.png‎| Scar tissue identification.
</gallery>

==Key Investigators==

* LiangJia Zhu, Allen Tannenbaum, UAB
* Yi Gao, BWH
* Josh Cates, Rob MacLeod, SCI

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* We are developing methods for identifying scar tissue from CARMA data. Our previous method demonstrates an effective identification ability for DE-MRI data. In this method, the intensity distribution inside the LA myocardial wall is modeled as a mixture of Gaussians. To improve the performance of this method, we will integrate the intensity information from the LA chamber into the overall identification procedure.
* We will discuss possible improvements for scar identification.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Design an identification scheme using the LA intensity as a prior
* Test the method using CARMA data
* Deliver the implementation in CLI module.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
*
</div>
</div>

2013 Project Week Template

2013-01-07T07:01:52Z

Zhulj: Undo revision 78967 by Zhulj (Talk)

2013 Project Week:CARMA Scar Segmentation

2013-01-01T16:37:49Z

Zhulj:

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
Image:ScarSeg EM.png| Scar tissue identification.
</gallery>

==Key Investigators==

* UAB: LiangJia Zhu, Allen Tannenbaum
* BWH:Yi Gao
* SCI: Josh Cates, Rob MacLeod
==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* The objective is to develop an effective scheme for identifying scar tissue. We will integrate the intensity distribution from LA chamber into the overall identification process.
* We will discuss possible improvements/extensions to this work.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Develop the scar identification as a CLI module in Slicer
* Test the method with CARMA data.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>

</div>
</div>

2013 Project Week:CARMA Scar Segmentation

2013-01-01T16:37:04Z

Zhulj:

2013 Project Week:CARMA Scar Segmentation

2013-01-01T16:29:27Z

Zhulj:

2013 Project Week Template

2013-01-01T04:08:31Z

Zhulj:

2013 Project Week Template

2013-01-01T04:07:57Z

Zhulj:

2013 Project Week Template

2013-01-01T04:07:38Z

Zhulj:

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
Image:ScarSeg_EM.png‎.png| Scar tissue identification.
</gallery>

==Key Investigators==

* LiangJia Zhu, Allen Tannenbaum, UAB
* Yi Gao, BWH
* Josh Cates, Rob MacLeod, SCI

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* We are developing methods for identifying scar tissue from CARMA data. Our previous method demonstrates an effective identification ability for DE-MRI data. In this method, the intensity distribution inside the LA myocardial wall is modeled as a mixture of Gaussians. To improve the performance of this method, we will integrate the intensity information from the LA chamber into the overall identification procedure.
* We will discuss possible improvements for scar identification.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Design an identification scheme using the LA intensity as a prior
* Test the method using CARMA data
* Deliver the implementation in CLI module.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Progress here
</div>
</div>

2013 Project Week:CARMA Scar Segmentation

2013-01-01T04:06:57Z

Zhulj:

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
Image:ScarSeg EM.png| Image description
</gallery>

==Key Investigators==

* Name, institution

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Objective here
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Planned work here
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Progress here
</div>
</div>

File:ScarSeg EM.png

2013-01-01T04:06:05Z

Zhulj: uploaded a new version of "File:ScarSeg EM.png"

2013 Project Week Template

2013-01-01T04:02:57Z

Zhulj:

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
Image:yourimagehere.png| Image description
</gallery>

==Key Investigators==

* LiangJia Zhu, Allen Tannenbaum, UAB
* Yi Gao, BWH
* Josh Cates, Rob MacLeod, SCI

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* We are developing methods for identifying scar tissue from CARMA data. Our previous method demonstrates an effective identification ability for DE-MRI data. In this method, the intensity distribution inside the LA myocardial wall is modeled as a mixture of Gaussians. To improve the performance of this method, we will integrate the intensity information from the LA chamber into the overall identification procedure.
* We will discuss possible improvements for scar identification.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Design an identification scheme using the LA intensity as a prior
* Test the method using CARMA data
* Deliver the implementation in CLI module.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Progress here
</div>
</div>

2013 Project Week Template

2013-01-01T04:01:47Z

Zhulj:

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
Image:yourimagehere.png| Image description
</gallery>

==Key Investigators==

* LiangJia Zhu, Allen Tannenbaum, UAB
* Yi Gao, BWH
* Josh Cates, Rob MacLeod, SCI

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* We are developing methods for identifying scar tissue from CARMA data. Our previous method demonstrates an effective identification ability for DE-MRI data. In this method, the intensity distribution inside the LA myocardial wall is modeled as a mixture of Gaussians. To improve the performance of this method, we will integrate the intensity information from the LA chamber into the overall identification procedure.
* We will discuss
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Design an identification scheme using the LA intensity as a prior
* Test the method using CARMA data
* Deliver the implementation in CLI module.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Progress here
</div>
</div>

2013 Project Week Template

2013-01-01T03:54:35Z

Zhulj:

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
Image:yourimagehere.png| Image description
</gallery>

==Key Investigators==

* LiangJia Zhu, Allen Tannenbaum, UAB
* Yi Gao, BWH
* Josh Cates, Rob MacLeod, SCI

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* We are developing methods for identifying scar tissue from CARMA data. Our previous method demonstrates an effective identification ability for DE-MRI data. In this method, the intensity distribution inside the LA myocardial wall is modeled as a mixture of Gaussians. To improve the performance of this method, we will integrate the intensity information from the LA chamber into the overall identification procedure.

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Planned work here
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Progress here
</div>
</div>

2013 Project Week Template

2013-01-01T03:53:03Z

Zhulj:

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
Image:yourimagehere.png| Image description
</gallery>

==Key Investigators==

* LiangJia Zhu, Allen Tannenbaum, UAB
* Yi Gao, BWH
* Josh Cates, Rob MacLeod, SCI

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* We are developing methods for identifying scar tissue from CARMA data. Our previous method demonstrates an effective identification ability for DE-MRI data. In this method, the intensity distribution inside the LA myocardial wall is modeled as a mixture of Gaussians. To improve the performance of this method, we will integrate the intensity information from the LA chamber as a prior.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Planned work here
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Progress here
</div>
</div>

2013 Project Week:CARMA Scar Segmentation

2013-01-01T03:44:37Z

Zhulj: Created page with '__NOTOC__ <gallery> Image:PW-SLC2013.png|Projects List Image:yourimagehere.png| Image description </gallery> ==Key Investigators== * Name,…'

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
Image:yourimagehere.png| Image description
</gallery>

==Key Investigators==

* Name, institution

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Objective here
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Planned work here
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Progress here
</div>
</div>

2013 Winter Project Week

2013-01-01T03:23:08Z

Zhulj: /* Atrial Fibrillation */

Back to [[Project Events]], [[Events]]
Back to [[Project Events]], [[AHM_2013]], [[Events]]

__NOTOC__
[[image:PW-SLC2013.png|300px]]

Project Week is a hands on activity -- programming using the open source [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the NA-MIC, NCIGT, and NAC calendars. It is held in the summer at MIT, typically the last week of June, and a shorter version is held in Salt Lake City in the winter, typically the second week of January.

Active preparation begins 6-8 weeks prior to the meeting, when a kick-off teleconference is hosted by the NA-MIC Engineering, Dissemination, and Leadership teams, the primary hosts of this event. Invitations to this call are sent to all na-mic members, past attendees of the event, as well as any parties who have expressed an interest in working with NA-MIC. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient NA-MIC coverage for all. Subsequent teleconferences allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams are asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself starts off with a short presentation by each project team, driven using their previously created description, and allows all participants to be acquainted with others who are doing similar work. In the rest of the week, about half the time is spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half is spent in project teams, doing hands-on programming, algorithm design, or clinical application of NA-MIC kit tools. The hands-on activities are done in 10-20 small teams of size 3-5, each with a mix of experts in NA-MIC kit software, algorithms, and clinical. To facilitate this work, a large room is setup with several tables, with internet and power access, and each team gathers on a table with their individual laptops, connects to the internet to download their software and data, and is able to work on their projects. On the last day of the event, a closing presentation session is held in which each project team presents a summary of what they accomplished during the week.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].
= Dates.Venue.Registration =

Please [[AHM_2013#Dates_Venue_Registration|click here for Dates, Venue, and Registration]] for this event.

= [[AHM_2013#Agenda|'''AGENDA''']] and Project List=

Please:
* [[AHM_2013#Agenda|'''Click here for the agenda for AHM 2013 and Project Week''']].
* [[#Projects|'''Click here to jump to Project list''']]

=Background and Preparation=

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]

=Projects=
* [[2013_Project_Week_Template | Template for project pages]]

==TBI==
* [[4D DTI tractography in Slicer for TBI]] (Bo Wang, Andrei Irimia, Micah Chambers, Jack Van Horn, Bo Wang, Marcel Prastawa, Guido Gerig)
* [[Clinically oriented TBI connectivity analysis in Slicer]] (Andrei Irimia, Bo Wang, Micah Chambers, Jack Van Horn, Marcel Prastawa, Guido Gerig)
* [[Geometric metamorphosis for TBI]] (Danielle Pace, Stephen Aylward, Andrei Irimia, Micah Chambers)

==Atrial Fibrillation==
* [[2013_Project_Week:CARMA_Scar_Segmentation|Scar Identification (LiangJia Zhu, Yi Gao, Josh Cates, Rob MacLeod, Allen Tannenbaum)]]
* [[2013_Project_Week:CARMA_PV_Antrum_Cut|SLICER MODULE: PV Antrum Cut Filter (Salma Bengali, Alan Morris, Josh Cates, Rob MacLeod)]]
* [[2013_Project_Week:CARMA_LA_Seg_Gopal|SLICER MODULE: Automated LA Segmentation by Gopal (Gopal Veni, Salma Bengali, Greg Gardner, Alan Morris, Josh Cates, Rob MacLeod)]]
* [[2013_Project_Week:CARMA_2D_Dilate|SLICER MODULE: 2D Dilate Filter (Salma Bengali, Greg Gardner, Alan Morris, Josh Cates, Rob MacLeod)]]
* [[2013_Project_Week:CARMA_PractialLASeg|SLICER WORKFLOW: Practical Manual LA Segmentation in Slicer (Salma Bengali, Greg Gardner, Alan Morris, Josh Cates, Rob MacLeod)]]
* [[2013_Project_Week:CARMA_RT_MRI|PROJECT PLANNING: Real Time MRI in Slicer with OpenIGT (Alan Morris, Ashvin George, Josh Cates, Rob MacLeod)]]

==Huntington's Disease==
* [[2013_Project_Week:QualityAssuranceModule|Quality assurance module enhancements]] (Dave Welch, Hans Johnson)
* [[2013_Project_Week:PythonModules|Slicer interface to add Python modules to Slicer environment]] (Dave Welch)
* [[2013_Project_Week:FastFiducialRegistrationModule|Fast fiducial registration module]] (Dave Welch, Nicole Aucoin)

==Head and Neck Cancer==
* [[2013_Winter_Project_Week:MABS|Multi atlas-based segmentation]] (Sharp, Shusharina, Golland)
* [[2013_Winter_Project_Week:Hybrid_registration|Hybrid interactive-automatic registration]] (Shusharina, Sharp, Pieper)

==Prostate Interventions==
* BRAINSFit in ITK4: extra functionality and testing for prostate MR registration (Andrey, Hans)
* PkModeling for prostate DCE MRI (Jim, Andrey)

==Abdominal Interventions==
* Abdominal navigation module (Junichi Tokuda, Atsushi Yamada)

==Medical Robotics==
* [[Configurable fiducial-based device to image registration]] (Junichi Tokuda)
* Reviving ultrasound integration for visual servoing (Laurent Chauvin, Noby Hata)
* [[2013_Project_Week:AgileMedicalRobot | Agile medical robotics development with Slier: Case study with MRI compatible robot(Taka Kato, Atsushi Yamada, Noby Hata, Kazuhumi Onuma)]]
* Investigating role of open source in translational research (Miki Kumekawa)

==General Image Guided Therapy==
* United SlicerIGT extension, repository, website (Tamas Ungi, Junichi Tokuda, Adam Rankin)
* Mobile image overlay augmented reality needle guidance (Adam Rankin, Tamas Ungi)

==Radiation Therapy==
* [[2013_Project_Week:DicomRtExport|SlicerRT: DICOM RT export]] (Kevin Wang, Greg Sharp, Csaba Pinter)
* [[2013_Project_Week:RtBeamGeometry|SlicerRT: Beam geometry widgets]] (Csaba Pinter, Greg Sharp)
* [[2013_Project_Week:MRIAblation|Thermal monitoring tools for MRI-guided laser ablation therapy of brain tumor]] (Laurent Chauvin)

==NA-MIC Kit Internals==
*[[2013_Project_Week:WebbasedAnatomicalTeachingFramework|Web-based anatomical teaching framework for mi2b2]] (Daniel Haehn, Steve Pieper, Sonia Pujol, Randy Gollub, Rudolph Pienaar, Ellen Grant)
*[[2013_Project_Week:ITKv4Default|ITKv4 Default in Superbuild]] - [http://www.na-mic.org/Bug/search.php?project_id=3&sticky_issues=on&sortby=last_updated&dir=DESC&hide_status_id=80&tag_string=ITKv4 Issues] (Matt McCormick, Luis Ibanez, Hans Johnson, Jc?, Bill Lorensen,Bradley Lowekamp)
*[[2013_Project_Week:MarkupsModule|Slicer4 Markups Module]] (Nicole Aucoin)
*[[2013_Project_Week:ColorHierarchies|Slicer4 Color Hierarchies]] (Nicole Aucoin)
*[[2013_Project_Week:PatientHierarchy|Slicer4 Patient Hierarchy]] (Csaba Pinter, Andrey Fedorov?, Nicole Aucoin, Steve Pieper)
*[[2013_Project_Week:ScalarBar|Slicer4 Scalar bar improvements]] (Kevin Wang, Nicole Aucoin Csaba Pinter)
*[[2013_Project_Week:CliMatlabInterface|Slicer4 CLI Matlab interface]] (Kevin Wang, Steve Pieper, Csaba Pinter)
*[[2013_Project_Week:PETStandardUptakeValueComputation| PET/CT SUV Module for Clinicians]] (Sonia Pujol, Markus Van Tol, Nicole Aucoin)
*[[2013_Project_Week:SelfTests|Slicer4 Self Test and Sample Data Refactor]] (Steve Pieper, Jim Miller, Jc, Sankhesh Jhaveri)
*[[2013_Project_Week:SimplifyMRMLReference|Simplify MRML References]] - Issue [http://www.na-mic.org/Bug/view.php?id=2727 #2727] (Alex Yarmarkovich, Andras Lasso?, Steve Pieper, Julien Finet ?, Sankhesh Jhaveri ?, Jc ?)
*[[2013_Project_Week:SlicerIPythonIntegration|Integration of IPython]] (Jc, Hans Johnson, Dave Welch, Steve Pieper, Matt McCormick)
*[[2013_Project_Week:SlicerDebianPackage|Slicer Debian package]] (Jc, Dominique Belhachemi ?, Greg Sharp)
*[[2013_Project_Week:SimplifyRendererMouseInteraction|Simplify renderer window mouse interaction]] - Mailing list [http://slicer-devel.65872.n3.nabble.com/Left-mouse-button-changes-window-level-Is-it-good-tt4026815.html thread] (Csaba ?, Greg?, Andriy?, Steve, Jc)
*[[2013_Project_Week:PotentialSolutionForDefiningRoleAttributesForVolumes|Potential solutions for defining roles and/or attributes for volumes that are preserved when the volume is processed.]] - Mailing list [http://slicer-devel.65872.n3.nabble.com/Volume-node-subclass-tt4026807.html thread] (Andras?, Greg?, Andriy?, Steve, Jc)
*[[2013_Project_Week:SteeredRegistration|Interactive Registration for Image Guided Therapy]] (Dirk Padfield, Jim Miller, Steve Pieper, Kunlin Cao)
*[[2013_Project_Week:Threaded SimpleITK Modules|Threaded SimpleITK Modules]] (Brad Lowekamp, Steve Pieper)

Algorithm:BU

2012-12-27T02:00:11Z

Zhulj: /* Ventricles Segmentation for Diagnosis of Cardiac Diseases */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Boston University/UAB Algorithms (PI: Allen Tannenbaum) =

At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Boston University/UAB Projects =

{| cellpadding="10" style="text-align:left;"

|-

| | [[Image:LiverFibrosisHist.png|200px|]]
| |

== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==

In this project, we provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data
has much lower resolution than histological image data, but it includes the entire liver
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)
in diﬀusion MRI correlates with the ﬁbrosis stage. [[Projects:LiverFibrosisStaging|More...]]

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== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]

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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==

Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension
to the principal axis transformation method for ﬁnding robust rigid transformation of two
volumes. The additional elastic registration is based on a volume registration method
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]

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== [[Projects:SobolevTracker|Object Tracking With Adaptive Sobolev Active Contours]] ==

In this project we propose adaptive tracking mechanism which can be used in medical video applications, or 3D volume segmentation. The proposed Sobolev active contour model overcomes the
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and
low contrast objects. Experimental results show the advantages of our approach compared
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]

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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==

We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured. [[Projects:MultiScaleShapeSegmentation|More...]]

<font color="red">'''New: '''</font> Gao, Y. and Corn, B. and Schifter, D. and Tannenbaum, A. Multiscale 3D Shape Representation and Segmentation with Applications to Hippocampal/Caudate Extraction from Brain MRI, Medical Image Analysis. 16(2) pp374, 2012

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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

<font color="red">'''New: '''</font> Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.

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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.

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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.

<font color="red">'''New: '''</font> Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .

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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==

The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), submitted.

<font color="red">'''New: '''</font>L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]

<font color="red">'''New: '''</font> Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.

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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]

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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), submitted.
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.

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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

<font color="red">'''New: '''</font> Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation. Neuroimage, volume 45, 2009, pp. 123-132.

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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.

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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)

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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==

An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]

<font color="red">'''New: '''</font> I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.

<font color="red">'''New: '''</font> P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.

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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==

A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]

<font color="red">'''New: '''</font> I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.

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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.

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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115.

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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.

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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.

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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

<font color="red">'''New: '''</font> X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.

<font color="red">'''New: '''</font> Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.

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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.

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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==

The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.

<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.

<font color="red">'''New: '''</font> Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.

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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

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Projects:LeftAtriumSegmentation

2012-12-27T01:59:22Z

Zhulj: /* Publications */

Back to [[Algorithm:BU|Boston University Algorithms]]
__NOTOC__
= Automatic Segmentation of Left Atrium via Variational Region Growing=

= Description =
Automatic segmentation of the left atrium (LA) from MRI data is a challenging but major task in medical imaging
analysis. An important application is concerned with the treatment of left atrial fibrillation [1]. Atrial fibrillation is a cardiac
arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart. One of the treatments
for such arrhythmia is the catheter ablation, which targets specific parts of the LA for radio-frequency ablation using
an intracardiac catheter [2]. Application of radio-frequency energy to the cardiac tissue causes thermal injury, which in
turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and
effectively cure atrial fibrillation. In order to perform such ablation, the extraction of the LA from the late gadolinium
enhancement MR (LGE-MR) images is required and is often performed manually, which is a very time-consuming task.
However, automatic LA segmentation is challenging due to the following factors: 1) the LA sized is relatively small as
compared to the left ventricle (LV) or lungs in cardiac MRI images; 2) boundaries are not clearly defined when the blood
pool goes into the pulmonary veins from the LA; 3) the shape variability of LA is large across subjects.

We propose an automatic approach for segmenting the left atrium from magnetic resonance imagery
(MRI). The segmentation problem is formulated as a problem in variational region growing. In particular, the method starts
locally by searching for a seed region of the left atrium from a given MR slice. A global constraint is imposed by applying a
shape prior to the left atrium represented by Zernike moments. The overall growing process is guided by the robust statistics of
intensities from the seed region along with the shape prior to capture the whole atrial region.

The proposed method consists of two key steps:
*(1) search for a seed region of the LA from an image slice in the axial view.

*(2) explore the LA region using a variational region-growing process. A shape prior is employed to drive the growing process towards atrium-like shapes.
Given a properly set seed region, a growing process driven by the robust statistics of the seed region is employed to explore the entire LA region. However, leakage is almost
inevitable because the statistics computed does not provide a global shape constraint on evolving contours. Hence, a shape
prior is applied to attract the growing process towards an expected shape.

= Results =
*[[File:Image-LASegWithMomentsPrior.png|800px]]
Region-growing process driven by robust statistics and Zernike moments shape prior.

*[[File:LASegRG2Atlas.png|800px]]
Comparison of the worst results obtained using the proposed method (first column) and the atlas-based method (second column) . From top to bottom:
the LA returned using the proposed method (red) and atlas-based method (green) in axial, coronal, and sagittal views, respectively.

= Key Investigators =
*Georgia Tech: Liangjia Zhu and Anthony Yezzi
*BWH: Yi Gao
*Boston University: Allen Tannenbaum

= Publications =
L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), submitted.

L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

= References =
1. C. J. McGann, E. G. Kholmovski, R. S. Oakes, J. E. Blauer, Segerson N. M. Daccarett, M., K. J. Airey, N. Akoum, E. N. Fish, T. J. Badger,
E. V. R. DiBella, D. Parker, R. S. MacLeod, and N. F. Marrouche. New magnetic resonance imaging based method to define extent of left atrial wall injury after the ablation of atrial fibrillation. Journal of the American College of Cardiology, 2008.

2. P. Jais, R. Weerasooriya, D.C. Shah, M. Hocini, L. Macle, K.J. Choi, C. Scavee, M. Ha ̈ssaguerre, and J. Clementy. Ablation therapy for atrial fibrillation (AF). Cardiovascular research, 54(2):337–346, 2002.

Algorithm:BU

2012-12-27T01:58:57Z

Zhulj: /* Left Atrium Segmentation for Atrial Fibrillation Treatment */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Boston University/UAB Algorithms (PI: Allen Tannenbaum) =

At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Boston University/UAB Projects =

{| cellpadding="10" style="text-align:left;"

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== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==

In this project, we provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data
has much lower resolution than histological image data, but it includes the entire liver
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)
in diﬀusion MRI correlates with the ﬁbrosis stage. [[Projects:LiverFibrosisStaging|More...]]

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== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]

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| | [[Image:toT1e1.png|200px|]]
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==

Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension
to the principal axis transformation method for ﬁnding robust rigid transformation of two
volumes. The additional elastic registration is based on a volume registration method
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]

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| | [[Image:HandTracking.png|200px|]]
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== [[Projects:SobolevTracker|Object Tracking With Adaptive Sobolev Active Contours]] ==

In this project we propose adaptive tracking mechanism which can be used in medical video applications, or 3D volume segmentation. The proposed Sobolev active contour model overcomes the
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and
low contrast objects. Experimental results show the advantages of our approach compared
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]

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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==

We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured. [[Projects:MultiScaleShapeSegmentation|More...]]

<font color="red">'''New: '''</font> Gao, Y. and Corn, B. and Schifter, D. and Tannenbaum, A. Multiscale 3D Shape Representation and Segmentation with Applications to Hippocampal/Caudate Extraction from Brain MRI, Medical Image Analysis. 16(2) pp374, 2012

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| | [[Image:3D_Segmentation_LA.png|200px]]
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

<font color="red">'''New: '''</font> Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.

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| | [[Image:MultiObjSeg.png|200px|]]
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.

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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.

<font color="red">'''New: '''</font> Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .

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| | [[File:LASegAxialView.png|200px]]
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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==

The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), submitted.

<font color="red">'''New: '''</font>L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]

<font color="red">'''New: '''</font> Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.

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| | [[Image:LongitudinalAFib.png|200px]]
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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]

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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), In preparation.

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| | [[Image:RiskMassSeg.png|200px]]
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.

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| | [[Image:GT-DWI-Reorientation-1.jpg|300px]]
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

<font color="red">'''New: '''</font> Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation. Neuroimage, volume 45, 2009, pp. 123-132.

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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.

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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)

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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==

An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]

<font color="red">'''New: '''</font> I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.

<font color="red">'''New: '''</font> P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.

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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==

A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]

<font color="red">'''New: '''</font> I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.

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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.

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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115.

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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.

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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.

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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

<font color="red">'''New: '''</font> X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.

<font color="red">'''New: '''</font> Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.

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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.

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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==

The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.

<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.

<font color="red">'''New: '''</font> Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.

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| | [[Image:Gatech caudateBands.PNG|200px]]
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

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