Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

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== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

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== [[Projects:BrainConnectivity|Brain Connectivity]] ==

'''New: ''' A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

'''New: ''' A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

A. Venkataraman, K.R.A Van Dijk, R.L. Buckner, and P. Golland. Exploring functional connectivity in fMRI via clustering. In Proc. ICASSP: IEEE International Conference on Acoustics, Speech and Signal Processing, 441-444, 2009.

Y. Golland, P. Golland, S. Bentin, and R. Malach. Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems. Neuropsychologia, 46(2):540-553, 2008.

P. Golland, Y. Golland, R. Malach. Detection of Spatial Activation Patterns As Unsupervised Segmentation of fMRI Data. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 4791:110-118, 2007.

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== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
Placeholder for Ramesh and Danial
[[Projects:ConnectivityAtlas|More...]]

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== [[Projects:GiniContrast| Multi-variate activation detection]] ==
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[[Projects:GiniContrast|More...]]

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== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to automatically segment the scar created by RF ablation in delayed enhancement MR images acquired after the procedure. This will then provide surgeons with a visualization which will help them to rapidly evaluate the success of the procedure.
[[Projects:CardiacAblation|More...]]

'''New: ''' M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

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== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

'''New: ''' M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

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== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

'''New: ''' T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Special Issue of Medical Image Analysis (MedIA), 14(5):654-665, 2010.

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== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' Menze BH, Van Leemput K, Honkela A, Konukoglu E, Weber MA, Ayache N and Golland P. A generative approach for image-based modeling of tumor growth. Proc IPMI 2011. LNCS. 12p

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== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

'''New: ''' B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

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== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

'''New: ''' B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

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== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

Poynton C., Jenkinson M., Wells III W. Atlas-Based Improved Prediction of Magnetic Field Inhomogeneity for Distortion Correction of EPI Data. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 5761:951-959, 2009.

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== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation that is insensitive to B0 Inhomogeneity. In Proc. ISMRM: Int. Soc. of Magnetic Resonance in Medicine, 2011 (in press).

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== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

'''New: ''' G. Langs, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Functional Geometry Alignment for Localization of Brain Areas. To appear in Proc. NIPS: Neural Information Processing Systems, 2010.

'''New: ''' D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

'''New: ''' D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

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== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

'''New: ''' W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

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== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, and B. Fischl. Automated Segmentation of Hippocampal Subfields from Ultra-High Resolution In Vivo MRI. Hippocampus, 19:549-557, 2009.

K. Van Leemput. Encoding Probabilistic Atlases Using Bayesian Inference. IEEE Transactions on Medical Imaging, 28(6):822-837, 2009.

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== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging, 28(9):1473 - 1487, 2009.

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== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

Asymmetric Image-Template Registration, M.R. Sabuncu, B.T. Thomas Yeo, T. Vercauteren, K. Van Leemput, P. Golland. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 5761:565-573, 2009.

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== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

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== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

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== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

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== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

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== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

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== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

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== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

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== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

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== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

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File:Mdepa scar DE-MRI projection.png

2011-03-30T23:10:14Z

Mdepa:

Projects:CardiacAblation

2011-03-30T23:07:12Z

Mdepa:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Left atrium segmentation =

The high anatomical variability of the heart’s left atrium makes its segmentation a particularly difficult problem. Specifically, the shape of the left atrium cavity, as well as the number and locations of the pulmonary veins connecting to it, vary substantially across subjects. We propose and demonstrate a robust atlas-based method for automatic segmentation of the left atrium in contrast-enhanced magnetic resonance angiography (MRA) images.

== Description ==

We perform the segmentation via a label fusion algorithm [1] that uses a training set of MRA images of different patients with corresponding manual segmentations. We first align the training images to the test subject image to be segmented and apply the resulting deformations to the corresponding manual segmentation label maps to yield a set of left atrium segmentations in the coordinate space of the test subject. These form a non-parametric subject-specific statistical atlas. We then use a weighted voting algorithm to assign every voxel to the left atrium or to the background. The weighted label fusion scheme assigns higher weights to voxels in training segmentations that are located deeper within the structure of interest and that have similar intensities in training and test images. We also handle varying intensity distributions between images by incorporating iterative intensity equalization in a variant of the demons registration algorithm [2] used for the registration of the training images to the novel test image.

== Results ==

We show in Figure 1 below a qualitative comparison between manual left atrium segmentations and automatic segmentations produced by our approach.

[[File:Mdepa_MRA_seg_3D_comparison.png|500px|thumb|center|Figure 1: Qualitative evaluation of left atrium segmentations in three different subjects. First row shows expert manual segmentations. The corresponding automatic segmentations produced by our method are in the second row.]]

We compare our method of weighed voting (WV) label fusion to three alternative automatic atlas-based approaches: majority voting (MV) label fusion, parametric atlas thresholding (AT) and atlas-based EM-segmentation (EM). The majority voting label fusion is similar to weighted voting, except it assigns each voxel to the label that occurs most frequently in the registered training set at that voxel. We also construct a parametric atlas that summarizes all 16 subjects in a single template image and a probabilistic label map by performing groupwise registration to an average space. After registering this new atlas to the test subject, we segment the left atrium using two different approaches. In atlas thresholding, we simply threshold the warped probabilistic label map at 0.5 to obtain the segmentation. This baseline method is analogous to majority voting in the parametric atlas setting. We also use the parametric atlas as a spatial prior in a traditional model-based EM-segmentation. Note that this construction favors the baseline algorithms as it includes the test image in the registration of all subjects into the common coordinate frame.

In Figure 2 below, we show example segmentations comparing the automatic segmentations produced by these methods and expert manual segmentations.

[[File:Mdepa_MRA_seg_2D_comparison.png|600px|thumb|center|Figure 2: Example segmentations of four different subjects: (a) expert manual segmentation (MS), (b) weighted voting label fusion (WV), (c) majority voting label fusion (MV), (d) parametric atlas thresholding (AT) and (e) EM-segmentation using the parametric atlas as a spatial prior (EM).]]

Figure 3 reports the segmentation accuracy for each method, as measured by the volume overlap Dice scores. We also report the differences in segmentation accuracy between our method and the benchmark algorithms. To compute the difference between two methods, we subtract the Dice score of the second method from the score of the first for each subject. Our approach clearly outperforms other algorithms (WV vs. MV: p < 10−9, WV vs. AT: p < 0.002, WV vs. EM: p < 0.003; single-sided paired t-test). To focus the evaluation on the critical part of the structure, we manually isolate the pulmonary veins in each of the manual and automatic segmentations, and compare the Dice scores for these limited label maps. Again, we observe consistent improvements offered by our approach (WV vs. MV: p < 10−7, WV vs. AT: p < 10−7, WV vs. EM: p < 0.03; single-sided paired t-test). Since atlas-based EM-segmentation is an intensity based method, it performs relatively well in segmenting pulmonary veins, but suffers from numerous false positives in other areas, which lower its overall Dice scores.

[[File:Mdepa_MRA_seg_Dices.png|500px|thumb|center|Figure 3: Dice scores of results for weighted voting label fusion (WV), majority voting la- bel fusion (MV), parametric atlas thresholding (AT) and atlas-based EM-segmentation (EM). For each box plot, the central red line indicates the median, the boxes extend to the 25th and 75th percentiles, and the whiskers extend to the most extreme values not considered outliers, which are plotted as red crosses. Stars indicate that the weighted label fusion method achieves significantly more accurate segmentation than the baseline method (single-sided paired t-test, ∗: p < 0.05, ∗∗: p < 0.01).]]

== Conclusions ==

Experimental results illustrate the capacity of our method to handle high anatomical variability, yielding accurate segmentation and detecting all pul- monary veins in all subjects. By explicitly modeling the anatomical variability represented in the label maps and the corresponding training images, the proposed method outperforms traditional atlas-based segmentation algorithms and a simple label fusion benchmark.

= Cardiac ablation scar visualization =

Atrial fibrillation is one of the most common heart conditions and can have very serious consequences such as stroke and heart failure. A technique called catheter radio-frequency (RF) ablation has recently emerged as a treatment. It involves burning the cardiac tissue that is responsible for the fibrillation. Even though this technique has been shown to work fairly well on atrial fibrillation patients, repeat procedures are often needed to fully correct the condition because surgeons lack the necessary tools to quickly evaluate the success of the procedure.

We propose a method to automatically visualize the scar created by RF ablation in delayed enhancement MR images acquired after the procedure. This will provide surgeons with a visualization showing the size, shape and location of the scar, which is information central to evaluating the outcome of the procedure.

== Description ==

The visualization of cardiac scars resulting from ablation procedures in delayed enhancement magnetic resonance images (DE-MRI) is a very challenging problem because of intersubject anatomical variability of the left atrium body and the pulmonary veins, the variation in the shape and location of the scars and tissue that appears enhanced in DE-MRI images even though it is not ablation scar. In addition, visualization is also challenging because even the most advanced acquisition techniques yield DE-MRI images with relatively poor contrast.

With all of these difficulties, performing this segmentation without exploiting some prior knowledge or significant feedback from the user is extremely challenging. Most previous attempts to segment scar in DE-MRI images relied heavily on input from the user. In contrast, we avoid this by automatically segmenting the left atrium in the DE-MRI images of the patients. The atrium segmentation provides us with prior
information about the location and shape of the left atrium, which in turn helps counter some of the challenges that were previously solved by requiring significant amounts of user interaction. We obtain this segmentation by first segmenting the left atrium in the MRA image of the patient’s heart using the method presented above. We then align the MRA image to the corresponding DE-MRI image of the same subject. With these two images aligned, we transfer the left atrium segmentation from the MRA to the DE-MRI image by applying the transformation computed in the registration.

== Results ==

After obtaining the segmentation of the left atrium in the DE-MRI image, we produce a visualization of the ablation scar by simply projecting the DE-MRI data onto the left atrium surface. We restrict the projection to only use image voxels within the empirically determined distance of 7mm of each side of the left atrium surface. Figure 4 below illustrates the maximum intensity projection results for one subject. In addition, we automatically threshold these projection values by computing the 75th percentile and show the resulting visualization as well. For comparison, we also project the expert manual scar segmentation onto the same left atrium surface.

[[File:Mdepa_scar_visualization.png|500px|thumb|center|Figure 4: Comparison of projections of DE-MRI data and manual scar segmentation onto left atrium surface. Circled area indicates acquisition artifact that causes non-scar tissue to be appear enhanced in the DE-MRI image.]]

We confirm visually that the thresholded projection values correlate well with the manual scar segmentations. Nevertheless, there is one area, which we circled in the figure, where these two differ considerably. This discrepancy is due to an imaging artifact caused by the acquisition protocol and is likely to present in any intensity-based algorithm.

== Conclusions ==

We visualize the ablation scars by performing a maximum intensity projection of the DE-MRI image onto the automatically generated surface of the left atrium. The visualization is further improved by thresholding the projection. We showed visually that both visualizations correlate well with the expert manual segmentation of the ablation scars.

= Literature =
[1] Nonparametric Mixture Models for Supervised Image Parcellation, M.R. Sabuncu, B.T.T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. PMMIA Workshop at MICCAI 2009.

[2] Diffeomorphic demons: Efficient non-parametric image registration. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N. NeuroImage 45(1), S61–S72 (2009)

= Key Investigators =

*MIT: [http://people.csail.mit.edu/mdepa/ Michal Depa] and Polina Golland

*BWH: Ehud Schmidt and Ron Kikinis

= Publications =

[http://www.na-mic.org/publications/pages/display?search=Projects%3ACardiacAblation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Segmentation and Visualization for Cardiac Ablation Procedures]

File:Mdepa scar visualization.png

2011-03-30T23:07:06Z

Mdepa: uploaded a new version of "File:Mdepa scar visualization.png"

2011-03-30T22:34:38Z

Mdepa:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Left atrium segmentation =

The high anatomical variability of the heart’s left atrium makes its segmentation a particularly difficult problem. Specifically, the shape of the left atrium cavity, as well as the number and locations of the pulmonary veins connecting to it, vary substantially across subjects. We propose and demonstrate a robust atlas-based method for automatic segmentation of the left atrium in contrast-enhanced magnetic resonance angiography (MRA) images.

== Description ==

We perform the segmentation via a label fusion algorithm [1] that uses a training set of MRA images of different patients with corresponding manual segmentations. We first align the training images to the test subject image to be segmented and apply the resulting deformations to the corresponding manual segmentation label maps to yield a set of left atrium segmentations in the coordinate space of the test subject. These form a non-parametric subject-specific statistical atlas. We then use a weighted voting algorithm to assign every voxel to the left atrium or to the background. The weighted label fusion scheme assigns higher weights to voxels in training segmentations that are located deeper within the structure of interest and that have similar intensities in training and test images. We also handle varying intensity distributions between images by incorporating iterative intensity equalization in a variant of the demons registration algorithm [2] used for the registration of the training images to the novel test image.

== Results ==

We show in Figure 1 below a qualitative comparison between manual left atrium segmentations and automatic segmentations produced by our approach.

[[File:Mdepa_MRA_seg_3D_comparison.png|800px|thumb|center|Figure 1: Qualitative evaluation of left atrium segmentations in three different subjects. First row shows expert manual segmentations. The corresponding automatic segmentations produced by our method are in the second row.]]

We compare our method of weighed voting (WV) label fusion to three alternative automatic atlas-based approaches: majority voting (MV) label fusion, parametric atlas thresholding (AT) and atlas-based EM-segmentation (EM). The majority voting label fusion is similar to weighted voting, except it assigns each voxel to the label that occurs most frequently in the registered training set at that voxel. We also construct a parametric atlas that summarizes all 16 subjects in a single template image and a probabilistic label map by performing groupwise registration to an average space. After registering this new atlas to the test subject, we segment the left atrium using two different approaches. In atlas thresholding, we simply threshold the warped probabilistic label map at 0.5 to obtain the segmentation. This baseline method is analogous to majority voting in the parametric atlas setting. We also use the parametric atlas as a spatial prior in a traditional model-based EM-segmentation. Note that this construction favors the baseline algorithms as it includes the test image in the registration of all subjects into the common coordinate frame.

In Figure 2 below, we show example segmentations comparing the automatic segmentations produced by these methods and expert manual segmentations.

[[File:Mdepa_MRA_seg_2D_comparison.png|800px|thumb|center|Figure 2: Example segmentations of four different subjects: (a) expert manual segmentation (MS), (b) weighted voting label fusion (WV), (c) majority voting label fusion (MV), (d) parametric atlas thresholding (AT) and (e) EM-segmentation using the parametric atlas as a spatial prior (EM).]]

Figure 3 reports the segmentation accuracy for each method, as measured by the volume overlap Dice scores. We also report the differences in segmentation accuracy between our method and the benchmark algorithms. To compute the difference between two methods, we subtract the Dice score of the second method from the score of the first for each subject. Our approach clearly outperforms other algorithms (WV vs. MV: p < 10−9, WV vs. AT: p < 0.002, WV vs. EM: p < 0.003; single-sided paired t-test). To focus the evaluation on the critical part of the structure, we manually isolate the pulmonary veins in each of the manual and automatic segmentations, and compare the Dice scores for these limited label maps. Again, we observe consistent improvements offered by our approach (WV vs. MV: p < 10−7, WV vs. AT: p < 10−7, WV vs. EM: p < 0.03; single-sided paired t-test). Since atlas-based EM-segmentation is an intensity based method, it performs relatively well in segmenting pulmonary veins, but suffers from numerous false positives in other areas, which lower its overall Dice scores.

[[File:Mdepa_MRA_seg_Dices.png|800px|thumb|center|Figure 3: Dice scores of results for weighted voting label fusion (WV), majority voting la- bel fusion (MV), parametric atlas thresholding (AT) and atlas-based EM-segmentation (EM). For each box plot, the central red line indicates the median, the boxes extend to the 25th and 75th percentiles, and the whiskers extend to the most extreme values not considered outliers, which are plotted as red crosses. Stars indicate that the weighted label fusion method achieves significantly more accurate segmentation than the baseline method (single-sided paired t-test, ∗: p < 0.05, ∗∗: p < 0.01).]]

= Cardiac ablation scar visualization =

Atrial fibrillation is one of the most common heart conditions and can have very serious consequences such as stroke and heart failure. A technique called catheter radio-frequency (RF) ablation has recently emerged as a treatment. It involves burning the cardiac tissue that is responsible for the fibrillation. Even though this technique has been shown to work fairly well on atrial fibrillation patients, repeat procedures are often needed to fully correct the condition because surgeons lack the necessary tools to quickly evaluate the success of the procedure.

We propose a method to automatically segment the scar created by RF ablation in delayed enhancement MR images acquired after the procedure. This will provide surgeons with a visualization showing the size, shape and location of the scar, which is information central to evaluating the outcome of the procedure.

== Description ==

We work with two types of images for each patient: MR angiography (MRA) images where the blood pool has a higher intensity than surrounding tissue and post-procedure delayed enhancement MR images (DE-MRI) where a contrast agent has been injected into the patient to enhance the ablation scar. Our approach is to first segment the left atrium in the MRA images using the label fusion algorithm described in [1]. We then transfer this segmentation to the DE-MRI image of the same patient by registering the two images.

Since the ablation scar we are trying to segment is known to be located on the left atrium myocardium, we use this spatial prior information to reduce the search space for the ablation scar to only a small vicinity of the left atrium surface. This avoids many false positives caused by the noise in the DE-MRI images. We will be exploring different segmentation methods in this ongoing work.

== Results ==

Here we present results we have obtained for one subject using our methods. In the following images, we show the left atrium segmentation in the MRA image as an outline in one slice as well as a 3D model.

{|
| [[Image:Mdepa_MRA_seg.png|200px]]
| [[Image:Mdepa_MRA_seg_3D.png|250px]]
|}

We then align the MRA and DE-MRI images of the same patient, which allows us to transfer the left atrium segmentation using the resulting deformation field. This is shown in the following figures.

{|
| [[Image:Mdepa_MRA_MDE_registration.png|320px]]
| [[Image:Mdepa_MDE_la_segmentation.png|250px]]
|}

Finally, we show some preliminary results of our cardiac ablation scar segmentation obtained using this spatial prior knowledge and intensity thresholding. The figure on the right also shows an expert manual segmentation of the scar alongside our result.

{|
| [[Image:Mdepa_MDE_scar_segmentation.png‎|300px]]
| [[Image:Mdepa_MDE_scar_seg_3D.png|350px]]
|}

= Literature =
[1] Nonparametric Mixture Models for Supervised Image Parcellation, M.R. Sabuncu, B.T.T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. PMMIA Workshop at MICCAI 2009.
[2] Diffeomorphic demons: Efficient non-parametric image registration. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N. NeuroImage 45(1), S61–S72 (2009)

= Key Investigators =

*MIT: [http://people.csail.mit.edu/mdepa/ Michal Depa] and Polina Golland

*BWH: Ehud Schmidt and Ron Kikinis

= Publications =

[http://www.na-mic.org/publications/pages/display?search=Projects%3ACardiacAblation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Segmentation and Visualization for Cardiac Ablation Procedures]

File:Mdepa MRA seg Dices.png

2011-03-30T22:34:14Z

Mdepa:

Projects:CardiacAblation

2011-03-30T22:30:53Z

Mdepa:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Left atrium segmentation =

The high anatomical variability of the heart’s left atrium makes its segmentation a particularly difficult problem. Specifically, the shape of the left atrium cavity, as well as the number and locations of the pulmonary veins connecting to it, vary substantially across subjects. We propose and demonstrate a robust atlas-based method for automatic segmentation of the left atrium in contrast-enhanced magnetic resonance angiography (MRA) images.

== Description ==

We perform the segmentation via a label fusion algorithm [1] that uses a training set of MRA images of different patients with corresponding manual segmentations. We first align the training images to the test subject image to be segmented and apply the resulting deformations to the corresponding manual segmentation label maps to yield a set of left atrium segmentations in the coordinate space of the test subject. These form a non-parametric subject-specific statistical atlas. We then use a weighted voting algorithm to assign every voxel to the left atrium or to the background. The weighted label fusion scheme assigns higher weights to voxels in training segmentations that are located deeper within the structure of interest and that have similar intensities in training and test images. We also handle varying intensity distributions between images by incorporating iterative intensity equalization in a variant of the demons registration algorithm [2] used for the registration of the training images to the novel test image.

== Results ==

We show in Figure 1 below a qualitative comparison between manual left atrium segmentations and automatic segmentations produced by our approach.

[[File:Mdepa_MRA_seg_3D_comparison.png|800px|thumb|center|Figure 1: Qualitative evaluation of left atrium segmentations in three different subjects. First row shows expert manual segmentations. The corresponding automatic segmentations produced by our method are in the second row.]]

We compare our method of weighed voting (WV) label fusion to three alternative automatic atlas-based approaches: majority voting (MV) label fusion, parametric atlas thresholding (AT) and atlas-based EM-segmentation (EM). The majority voting label fusion is similar to weighted voting, except it assigns each voxel to the label that occurs most frequently in the registered training set at that voxel. We also construct a parametric atlas that summarizes all 16 subjects in a single template image and a probabilistic label map by performing groupwise registration to an average space. After registering this new atlas to the test subject, we segment the left atrium using two different approaches. In atlas thresholding, we simply threshold the warped probabilistic label map at 0.5 to obtain the segmentation. This baseline method is analogous to majority voting in the parametric atlas setting. We also use the parametric atlas as a spatial prior in a traditional model-based EM-segmentation. Note that this construction favors the baseline algorithms as it includes the test image in the registration of all subjects into the common coordinate frame.

In Figure 2 below, we show example segmentations comparing the automatic segmentations produced by these methods and expert manual segmentations.

[[File:Mdepa_MRA_seg_2D_comparison.png|800px|thumb|center|Figure 2: Example segmentations of four different subjects: (a) expert manual segmentation (MS), (b) weighted voting label fusion (WV), (c) majority voting label fusion (MV), (d) parametric atlas thresholding (AT) and (e) EM-segmentation using the parametric atlas as a spatial prior (EM).]]

= Cardiac ablation scar visualization =

Atrial fibrillation is one of the most common heart conditions and can have very serious consequences such as stroke and heart failure. A technique called catheter radio-frequency (RF) ablation has recently emerged as a treatment. It involves burning the cardiac tissue that is responsible for the fibrillation. Even though this technique has been shown to work fairly well on atrial fibrillation patients, repeat procedures are often needed to fully correct the condition because surgeons lack the necessary tools to quickly evaluate the success of the procedure.

We propose a method to automatically segment the scar created by RF ablation in delayed enhancement MR images acquired after the procedure. This will provide surgeons with a visualization showing the size, shape and location of the scar, which is information central to evaluating the outcome of the procedure.

== Description ==

We work with two types of images for each patient: MR angiography (MRA) images where the blood pool has a higher intensity than surrounding tissue and post-procedure delayed enhancement MR images (DE-MRI) where a contrast agent has been injected into the patient to enhance the ablation scar. Our approach is to first segment the left atrium in the MRA images using the label fusion algorithm described in [1]. We then transfer this segmentation to the DE-MRI image of the same patient by registering the two images.

Since the ablation scar we are trying to segment is known to be located on the left atrium myocardium, we use this spatial prior information to reduce the search space for the ablation scar to only a small vicinity of the left atrium surface. This avoids many false positives caused by the noise in the DE-MRI images. We will be exploring different segmentation methods in this ongoing work.

== Results ==

Here we present results we have obtained for one subject using our methods. In the following images, we show the left atrium segmentation in the MRA image as an outline in one slice as well as a 3D model.

{|
| [[Image:Mdepa_MRA_seg.png|200px]]
| [[Image:Mdepa_MRA_seg_3D.png|250px]]
|}

We then align the MRA and DE-MRI images of the same patient, which allows us to transfer the left atrium segmentation using the resulting deformation field. This is shown in the following figures.

{|
| [[Image:Mdepa_MRA_MDE_registration.png|320px]]
| [[Image:Mdepa_MDE_la_segmentation.png|250px]]
|}

Finally, we show some preliminary results of our cardiac ablation scar segmentation obtained using this spatial prior knowledge and intensity thresholding. The figure on the right also shows an expert manual segmentation of the scar alongside our result.

{|
| [[Image:Mdepa_MDE_scar_segmentation.png‎|300px]]
| [[Image:Mdepa_MDE_scar_seg_3D.png|350px]]
|}

= Literature =
[1] Nonparametric Mixture Models for Supervised Image Parcellation, M.R. Sabuncu, B.T.T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. PMMIA Workshop at MICCAI 2009.
[2] Diffeomorphic demons: Efficient non-parametric image registration. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N. NeuroImage 45(1), S61–S72 (2009)

= Key Investigators =

*MIT: [http://people.csail.mit.edu/mdepa/ Michal Depa] and Polina Golland

*BWH: Ehud Schmidt and Ron Kikinis

= Publications =

[http://www.na-mic.org/publications/pages/display?search=Projects%3ACardiacAblation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Segmentation and Visualization for Cardiac Ablation Procedures]

Projects:CardiacAblation

2011-03-30T22:29:31Z

Mdepa:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Left atrium segmentation =

The high anatomical variability of the heart’s left atrium makes its segmentation a particularly difficult problem. Specifically, the shape of the left atrium cavity, as well as the number and locations of the pulmonary veins connecting to it, vary substantially across subjects. We propose and demonstrate a robust atlas-based method for automatic segmentation of the left atrium in contrast-enhanced magnetic resonance angiography (MRA) images.

== Description ==

We perform the segmentation via a label fusion algorithm [1] that uses a training set of MRA images of different patients with corresponding manual segmentations. We first align the training images to the test subject image to be segmented and apply the resulting deformations to the corresponding manual segmentation label maps to yield a set of left atrium segmentations in the coordinate space of the test subject. These form a non-parametric subject-specific statistical atlas. We then use a weighted voting algorithm to assign every voxel to the left atrium or to the background. The weighted label fusion scheme assigns higher weights to voxels in training segmentations that are located deeper within the structure of interest and that have similar intensities in training and test images. We also handle varying intensity distributions between images by incorporating iterative intensity equalization in a variant of the demons registration algorithm [2] used for the registration of the training images to the novel test image.

== Results ==

We show in Figure 1 below a qualitative comparison between manual left atrium segmentations and automatic segmentations produced by our approach.

[[File:Mdepa_MRA_seg_3D_comparison.png|800px|thumb|center|Figure 1: Qualitative evaluation of left atrium segmentations in three different subjects. First row shows expert manual segmentations. The corresponding automatic segmentations produced by our method are in the second row.]]

We compare our method of weighed voting (WV) label fusion to three alternative automatic atlas-based approaches: majority voting (MV) label fusion, parametric atlas thresholding (AT) and atlas-based EM-segmentation (EM). The majority voting label fusion is similar to weighted voting, except it assigns each voxel to the label that occurs most frequently in the registered training set at that voxel. We also construct a parametric atlas that summarizes all 16 subjects in a single template image and a probabilistic label map by performing groupwise registration to an average space. After registering this new atlas to the test subject, we segment the left atrium using two different approaches. In atlas thresholding, we simply threshold the warped probabilistic label map at 0.5 to obtain the segmentation. This baseline method is analogous to majority voting in the parametric atlas setting. We also use the parametric atlas as a spatial prior in a traditional model-based EM-segmentation. Note that this construction favors the baseline algorithms as it includes the test image in the registration of all subjects into the common coordinate frame.

In Figure 2 below, we show example segmentations comparing the automatic segmentations produced by these methods and expert manual segmentations.

[[File:Mdepa_MRA_seg_2D_comparison.png|800px|thumb|center|Figure 1: Qualitative evaluation of left atrium segmentations in three different subjects. First row shows expert manual segmentations. The corresponding automatic segmentations produced by our method are in the second row.]]

= Cardiac ablation scar visualization =

Atrial fibrillation is one of the most common heart conditions and can have very serious consequences such as stroke and heart failure. A technique called catheter radio-frequency (RF) ablation has recently emerged as a treatment. It involves burning the cardiac tissue that is responsible for the fibrillation. Even though this technique has been shown to work fairly well on atrial fibrillation patients, repeat procedures are often needed to fully correct the condition because surgeons lack the necessary tools to quickly evaluate the success of the procedure.

We propose a method to automatically segment the scar created by RF ablation in delayed enhancement MR images acquired after the procedure. This will provide surgeons with a visualization showing the size, shape and location of the scar, which is information central to evaluating the outcome of the procedure.

== Description ==

We work with two types of images for each patient: MR angiography (MRA) images where the blood pool has a higher intensity than surrounding tissue and post-procedure delayed enhancement MR images (DE-MRI) where a contrast agent has been injected into the patient to enhance the ablation scar. Our approach is to first segment the left atrium in the MRA images using the label fusion algorithm described in [1]. We then transfer this segmentation to the DE-MRI image of the same patient by registering the two images.

Since the ablation scar we are trying to segment is known to be located on the left atrium myocardium, we use this spatial prior information to reduce the search space for the ablation scar to only a small vicinity of the left atrium surface. This avoids many false positives caused by the noise in the DE-MRI images. We will be exploring different segmentation methods in this ongoing work.

== Results ==

Here we present results we have obtained for one subject using our methods. In the following images, we show the left atrium segmentation in the MRA image as an outline in one slice as well as a 3D model.

{|
| [[Image:Mdepa_MRA_seg.png|200px]]
| [[Image:Mdepa_MRA_seg_3D.png|250px]]
|}

We then align the MRA and DE-MRI images of the same patient, which allows us to transfer the left atrium segmentation using the resulting deformation field. This is shown in the following figures.

{|
| [[Image:Mdepa_MRA_MDE_registration.png|320px]]
| [[Image:Mdepa_MDE_la_segmentation.png|250px]]
|}

Finally, we show some preliminary results of our cardiac ablation scar segmentation obtained using this spatial prior knowledge and intensity thresholding. The figure on the right also shows an expert manual segmentation of the scar alongside our result.

{|
| [[Image:Mdepa_MDE_scar_segmentation.png‎|300px]]
| [[Image:Mdepa_MDE_scar_seg_3D.png|350px]]
|}

= Literature =
[1] Nonparametric Mixture Models for Supervised Image Parcellation, M.R. Sabuncu, B.T.T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. PMMIA Workshop at MICCAI 2009.
[2] Diffeomorphic demons: Efficient non-parametric image registration. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N. NeuroImage 45(1), S61–S72 (2009)

= Key Investigators =

*MIT: [http://people.csail.mit.edu/mdepa/ Michal Depa] and Polina Golland

*BWH: Ehud Schmidt and Ron Kikinis

= Publications =

[http://www.na-mic.org/publications/pages/display?search=Projects%3ACardiacAblation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Segmentation and Visualization for Cardiac Ablation Procedures]

Projects:CardiacAblation

2011-03-30T22:29:15Z

Mdepa:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Left atrium segmentation =

The high anatomical variability of the heart’s left atrium makes its segmentation a particularly difficult problem. Specifically, the shape of the left atrium cavity, as well as the number and locations of the pulmonary veins connecting to it, vary substantially across subjects. We propose and demonstrate a robust atlas-based method for automatic segmentation of the left atrium in contrast-enhanced magnetic resonance angiography (MRA) images.

== Description ==

We perform the segmentation via a label fusion algorithm [1] that uses a training set of MRA images of different patients with corresponding manual segmentations. We first align the training images to the test subject image to be segmented and apply the resulting deformations to the corresponding manual segmentation label maps to yield a set of left atrium segmentations in the coordinate space of the test subject. These form a non-parametric subject-specific statistical atlas. We then use a weighted voting algorithm to assign every voxel to the left atrium or to the background. The weighted label fusion scheme assigns higher weights to voxels in training segmentations that are located deeper within the structure of interest and that have similar intensities in training and test images. We also handle varying intensity distributions between images by incorporating iterative intensity equalization in a variant of the demons registration algorithm [2] used for the registration of the training images to the novel test image.

== Results ==

We show in Figure 1 below a qualitative comparison between manual left atrium segmentations and automatic segmentations produced by our approach.

[[File:Mdepa_MRA_seg_3D_comparison.png|800px|thumb|center|Figure 1: Qualitative evaluation of left atrium segmentations in three different subjects. First row shows expert manual segmentations. The corresponding automatic segmentations produced by our method are in the second row.]]

We compare our method of weighed voting (WV) label fusion to three alternative automatic atlas-based approaches: majority voting (MV) label fusion, parametric atlas thresholding (AT) and atlas-based EM-segmentation (EM). The majority voting label fusion is similar to weighted voting, except it assigns each voxel to the label that occurs most frequently in the registered training set at that voxel. We also construct a parametric atlas that summarizes all 16 subjects in a single template image and a probabilistic label map by performing groupwise registration to an average space. After registering this new atlas to the test subject, we segment the left atrium using two different approaches. In atlas thresholding, we simply threshold the warped probabilistic label map at 0.5 to obtain the segmentation. This baseline method is analogous to majority voting in the parametric atlas setting. We also use the parametric atlas as a spatial prior in a traditional model-based EM-segmentation. Note that this construction favors the baseline algorithms as it includes the test image in the registration of all subjects into the common coordinate frame.

In Figure 2 below, we show example segmentations comparing the automatic segmentations produced by these methods and expert manual segmentations.

[[File:Mdepa_MRA_seg_2D_comparison|800px|thumb|center|Figure 1: Qualitative evaluation of left atrium segmentations in three different subjects. First row shows expert manual segmentations. The corresponding automatic segmentations produced by our method are in the second row.]]

= Cardiac ablation scar visualization =

Atrial fibrillation is one of the most common heart conditions and can have very serious consequences such as stroke and heart failure. A technique called catheter radio-frequency (RF) ablation has recently emerged as a treatment. It involves burning the cardiac tissue that is responsible for the fibrillation. Even though this technique has been shown to work fairly well on atrial fibrillation patients, repeat procedures are often needed to fully correct the condition because surgeons lack the necessary tools to quickly evaluate the success of the procedure.

We propose a method to automatically segment the scar created by RF ablation in delayed enhancement MR images acquired after the procedure. This will provide surgeons with a visualization showing the size, shape and location of the scar, which is information central to evaluating the outcome of the procedure.

== Description ==

We work with two types of images for each patient: MR angiography (MRA) images where the blood pool has a higher intensity than surrounding tissue and post-procedure delayed enhancement MR images (DE-MRI) where a contrast agent has been injected into the patient to enhance the ablation scar. Our approach is to first segment the left atrium in the MRA images using the label fusion algorithm described in [1]. We then transfer this segmentation to the DE-MRI image of the same patient by registering the two images.

Since the ablation scar we are trying to segment is known to be located on the left atrium myocardium, we use this spatial prior information to reduce the search space for the ablation scar to only a small vicinity of the left atrium surface. This avoids many false positives caused by the noise in the DE-MRI images. We will be exploring different segmentation methods in this ongoing work.

== Results ==

Here we present results we have obtained for one subject using our methods. In the following images, we show the left atrium segmentation in the MRA image as an outline in one slice as well as a 3D model.

{|
| [[Image:Mdepa_MRA_seg.png|200px]]
| [[Image:Mdepa_MRA_seg_3D.png|250px]]
|}

We then align the MRA and DE-MRI images of the same patient, which allows us to transfer the left atrium segmentation using the resulting deformation field. This is shown in the following figures.

{|
| [[Image:Mdepa_MRA_MDE_registration.png|320px]]
| [[Image:Mdepa_MDE_la_segmentation.png|250px]]
|}

Finally, we show some preliminary results of our cardiac ablation scar segmentation obtained using this spatial prior knowledge and intensity thresholding. The figure on the right also shows an expert manual segmentation of the scar alongside our result.

{|
| [[Image:Mdepa_MDE_scar_segmentation.png‎|300px]]
| [[Image:Mdepa_MDE_scar_seg_3D.png|350px]]
|}

= Literature =
[1] Nonparametric Mixture Models for Supervised Image Parcellation, M.R. Sabuncu, B.T.T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. PMMIA Workshop at MICCAI 2009.
[2] Diffeomorphic demons: Efficient non-parametric image registration. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N. NeuroImage 45(1), S61–S72 (2009)

= Key Investigators =

*MIT: [http://people.csail.mit.edu/mdepa/ Michal Depa] and Polina Golland

*BWH: Ehud Schmidt and Ron Kikinis

= Publications =

[http://www.na-mic.org/publications/pages/display?search=Projects%3ACardiacAblation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Segmentation and Visualization for Cardiac Ablation Procedures]

File:Mdepa MRA seg 2D comparison.png

2011-03-30T22:28:32Z

Mdepa: Example segmentations of four different subjects: (a) expert manual segmentation (MS), (b) weighted voting label fusion (WV), (c) majority voting label fusion (MV), (d) parametric atlas thresholding (AT) and (e) EM-segmentation using the parametric atlas

2011-03-30T21:26:54Z

Mdepa: Created page with ' Back to NA-MIC Collaborations, MIT Algorithms, __NOTOC__ = Cardiac ablation scar segmentation = Atr…'

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Cardiac ablation scar segmentation =

Atrial fibrillation is one of the most common heart conditions and can have very serious consequences such as stroke and heart failure. A technique called catheter radio-frequency (RF) ablation has recently emerged as a treatment. It involves burning the cardiac tissue that is responsible for the fibrillation. Even though this technique has been shown to work fairly well on atrial fibrillation patients, repeat procedures are often needed to fully correct the condition because surgeons lack the necessary tools to quickly evaluate the success of the procedure.

We propose a method to automatically segment the scar created by RF ablation in delayed enhancement MR images acquired after the procedure. This will provide surgeons with a visualization showing the size, shape and location of the scar, which is information central to evaluating the outcome of the procedure.

= Description =

We work with two types of images for each patient: MR angiography (MRA) images where the blood pool has a higher intensity than surrounding tissue and post-procedure delayed enhancement MR images (DE-MRI) where a contrast agent has been injected into the patient to enhance the ablation scar. Our approach is to first segment the left atrium in the MRA images using the label fusion algorithm described in [1]. We then transfer this segmentation to the DE-MRI image of the same patient by registering the two images.

Since the ablation scar we are trying to segment is known to be located on the left atrium myocardium, we use this spatial prior information to reduce the search space for the ablation scar to only a small vicinity of the left atrium surface. This avoids many false positives caused by the noise in the DE-MRI images. We will be exploring different segmentation methods in this ongoing work.

== Results ==

Here we present results we have obtained for one subject using our methods. In the following images, we show the left atrium segmentation in the MRA image as an outline in one slice as well as a 3D model.

{|
| [[Image:Mdepa_MRA_seg.png|200px]]
| [[Image:Mdepa_MRA_seg_3D.png|250px]]
|}

We then align the MRA and DE-MRI images of the same patient, which allows us to transfer the left atrium segmentation using the resulting deformation field. This is shown in the following figures.

{|
| [[Image:Mdepa_MRA_MDE_registration.png|320px]]
| [[Image:Mdepa_MDE_la_segmentation.png|250px]]
|}

Finally, we show some preliminary results of our cardiac ablation scar segmentation obtained using this spatial prior knowledge and intensity thresholding. The figure on the right also shows an expert manual segmentation of the scar alongside our result.

{|
| [[Image:Mdepa_MDE_scar_segmentation.png‎|300px]]
| [[Image:Mdepa_MDE_scar_seg_3D.png|350px]]
|}

= Literature =
[1] Nonparametric Mixture Models for Supervised Image Parcellation, M.R. Sabuncu, B.T.T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. PMMIA Workshop at MICCAI 2009.

= Key Investigators =

*MIT: [http://people.csail.mit.edu/mdepa/ Michal Depa] and Polina Golland

*BWH: Ehud Schmidt and Ron Kikinis

= Publications =

[http://www.na-mic.org/publications/pages/display?search=Projects%3AAblationScarSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Left Atrium Segmentation]