NAMIC Wiki - User contributions [en]

Project Week 25/SALT Spatiotemporal Modeling:

2017-06-30T13:39:19Z

Jfishbaugh:

__NOTOC__

Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/james-fishbaugh James Fishbaugh] (NYU Tandon School of Engineering, USA)
*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|
* Slicer Shape AnaLysis Toolbox ([http://github.com/Kitware/SlicerSALT SlicerSALT]) is a standalone version of Slicer specifically for shape analysis, currently under development. One aspect of the project is the validation of the tools to be included in SlicerSALT. In this project, we will begin validating the shape regression component with a longitudinal database of subcortical structures.

* The longitudinal infant data has been collected as part of the NIH funded UNC Psychiatry CONTE project to study early brain development in infants at risk for mental illness (PI [https://www.med.unc.edu/psych/earlybraindevresearch/people/principal-investigators/john-gilmore John H. Gilmore]), Distinguished Professor, Director of the Early Brain Development Program, UNC Chapel Hill, Department of Psychiatry).

|

* Longitudinal dataset consists of 553 subjects with a total of 1530 scans. MR images are acquired at clustered ages of 1, 2, 4, 6, 8, and 10 years of age. Subjects commonly have missing data points, though many subjects have 3 or more more scans.

* We focus this week on getting the data organized, setting up the processing pipeline, as well as estimating a model for a single subject. We would also like to identify Slicer tools that are helpful for our analysis, to be considered for inclusion in Slicer SALT.

* Subcortical structures were segmented from infant MRI by the UNC Chapel Hill Psychiatry [https://www.med.unc.edu/psych/research/niral NIRAL] group of Martin Styner. Procedures included multi-atlas segmentation developed by NIRAL in collaboration with partner groups.

|

* We found that subject data is not aligned across time; the observations do not share a common coordinate system. For a given set of images, we used Slicer to place a fiducial in each image space. We then used a linear transform to manually move one fiducial onto the other, using the 3D view as a visual guide. The linear transform was used as an initialization for rigid registration (BRAINS) in Slicer, to compute the final rigid transformation between images. We use the image when the child is oldest as the fixed image, and register the rest of the time-series to this reference.

* Subcortical structures are extracted via marching cubes, found in the [https://github.com/BioMedIA/MIRTK MIRTK] package.

* Shapes are decimated and smoothed using [http://www.meshlab.net/ Meshlab].

* A spatiotemporal model was estimated for a single subject with 5 observations using [https://github.com/jamesfishbaugh/shape4D shape4d].

* Andras mentioned the sequences module can provide similar functionality to Paraview, which is something to keep in mind for including in Slicer SALT.

|}

==Illustrations==
Left: Acquisitions of a single subject with observations at 1, 2, 4, 6, and 8 years old. Right: Sub-cortical structures are segmented for each time point.
{|
|-
| [[File:Observations_in_time.gif|600px]] || [[File:8yr_with_labels.png|460px]]
|}

 
Continuous spatiotemporal model of all subcortical structures from 0 to 8 years of age. Color denotes speed in mm/year, with vectors showing direction of growth. Two views of the same growth trajectory is shown below, from the side and from the front.

{|
|-
| http://research.engineering.nyu.edu/~fishbaugh/docs/anim_glyphs_side_small.gif || http://research.engineering.nyu.edu/~fishbaugh/docs/anim_glyphs_front_small.gif
|}

==Background and References==

[1] Fishbaugh, J., Durrleman, S., Prastawa, M., Gerig, G. Geodesic shape regression with multiple geometries and sparse parameters. Medical Image Analysis. Vol 39. pp. 1-17. (2017)

[2] Fishbaugh, J., Durrleman, S., Gerig, G. Estimation of smooth growth trajectories with controlled acceleration from time series shape data. Proc. of Medical Image Computing and Computer Assisted Intervention (MICCAI '11). Vol 6892, pp. 401-408. (2011)

*[http://github.com/Kitware/SlicerSALT SlicerSALT]

Project Week 25/SALT Spatiotemporal Modeling:

2017-06-30T10:18:39Z

Jfishbaugh:

__NOTOC__

Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/james-fishbaugh James Fishbaugh] (NYU Tandon School of Engineering, USA)
*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|
* Slicer Shape AnaLysis Toolbox ([http://github.com/Kitware/SlicerSALT SlicerSALT]) is a standalone version of Slicer specifically for shape analysis, currently under development. One aspect of the project is the validation of the tools to be included in SlicerSALT. In this project, we will begin validating the shape regression component with a longitudinal database of subcortical structures.

* The longitudinal infant data has been collected as part of the NIH funded UNC Psychiatry CONTE project to study early brain development in infants at risk for mental illness (PI [https://www.med.unc.edu/psych/earlybraindevresearch/people/principal-investigators/john-gilmore John H. Gilmore]), Distinguished Professor, Director of the Early Brain Development Program, UNC Chapel Hill, Department of Psychiatry).

|

* Longitudinal dataset consists of 553 subjects with a total of 1530 scans. MR images are acquired at clustered ages of 1, 2, 4, 6, 8, and 10 years of age. Subjects commonly have missing data points, though many subjects have 3 or more more scans.

* We focus this week on getting the data organized, setting up the processing pipeline, as well as estimating a model for a single subject. We would also like to identify Slicer tools that are helpful for our analysis, to be considered for inclusion in Slicer SALT.

* Subcortical structures were segmented from infant MRI by the UNC Chapel Hill Psychiatry [https://www.med.unc.edu/psych/research/niral NIRAL] group of Martin Styner. Procedures included multi-atlas segmentation developed by NIRAL in collaboration with partner groups.

|

* We found that subject data is not aligned across time; the observations do not share a common coordinate system. For a given set of images, we used Slicer to place a fiducial in each image space. We then used a linear transform to manually move one fiducial onto the other, using the 3D view as a visual guide. The linear transform was used as an initialization for rigid registration (BRAINS) in Slicer, to compute the final rigid transformation between images. We use the image when the child is oldest as the fixed image, and register the rest of the time-series to this reference.

* Subcortical structures are extracted via marching cubes, found in the [https://github.com/BioMedIA/MIRTK MIRTK] package.

* Shapes are decimated and smoothed using [http://www.meshlab.net/ Meshlab].

* A spatiotemporal model was estimated for a single subject with 5 observations using [https://github.com/jamesfishbaugh/shape4D shape4d].

|}

==Illustrations==
Left) Acquisitions of a single subject with observations at 1, 2, 4, 6, and 8 years old. Right) Sub-cortical structures are segmented for each time point.
{|
|-
| [[File:Observations_in_time.gif|500px]] || [[File:8yr_with_labels.png|460px]]
|}

 
Continuous spatiotemporal model of all subcortical structures from 0 to 8 years of age. Color denotes speed in mm/year, with vectors showing direction of growth. Two views of the same growth trajectory is shown below, from the side and from the front.

{|
|-
| http://research.engineering.nyu.edu/~fishbaugh/docs/anim_glyphs_side_small.gif || http://research.engineering.nyu.edu/~fishbaugh/docs/anim_glyphs_front_small.gif
|}

==Background and References==

[1] Fishbaugh, J., Durrleman, S., Prastawa, M., Gerig, G. Geodesic shape regression with multiple geometries and sparse parameters. Medical Image Analysis. Vol 39. pp. 1-17. (2017)

[2] Fishbaugh, J., Durrleman, S., Gerig, G. Estimation of smooth growth trajectories with controlled acceleration from time series shape data. Proc. of Medical Image Computing and Computer Assisted Intervention (MICCAI '11). Vol 6892, pp. 401-408. (2011)

*[http://github.com/Kitware/SlicerSALT SlicerSALT]

Project Week 25/SALT Spatiotemporal Modeling:

2017-06-30T10:18:21Z

Jfishbaugh:

__NOTOC__

Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/james-fishbaugh James Fishbaugh] (NYU Tandon School of Engineering, USA)
*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|
Slicer Shape AnaLysis Toolbox ([http://github.com/Kitware/SlicerSALT SlicerSALT]) is a standalone version of Slicer specifically for shape analysis, currently under development. One aspect of the project is the validation of the tools to be included in SlicerSALT. In this project, we will begin validating the shape regression component with a longitudinal database of subcortical structures.

* The longitudinal infant data has been collected as part of the NIH funded UNC Psychiatry CONTE project to study early brain development in infants at risk for mental illness (PI [https://www.med.unc.edu/psych/earlybraindevresearch/people/principal-investigators/john-gilmore John H. Gilmore]), Distinguished Professor, Director of the Early Brain Development Program, UNC Chapel Hill, Department of Psychiatry).

|

* Longitudinal dataset consists of 553 subjects with a total of 1530 scans. MR images are acquired at clustered ages of 1, 2, 4, 6, 8, and 10 years of age. Subjects commonly have missing data points, though many subjects have 3 or more more scans.

* We focus this week on getting the data organized, setting up the processing pipeline, as well as estimating a model for a single subject. We would also like to identify Slicer tools that are helpful for our analysis, to be considered for inclusion in Slicer SALT.

* Subcortical structures were segmented from infant MRI by the UNC Chapel Hill Psychiatry [https://www.med.unc.edu/psych/research/niral NIRAL] group of Martin Styner. Procedures included multi-atlas segmentation developed by NIRAL in collaboration with partner groups.

|

* We found that subject data is not aligned across time; the observations do not share a common coordinate system. For a given set of images, we used Slicer to place a fiducial in each image space. We then used a linear transform to manually move one fiducial onto the other, using the 3D view as a visual guide. The linear transform was used as an initialization for rigid registration (BRAINS) in Slicer, to compute the final rigid transformation between images. We use the image when the child is oldest as the fixed image, and register the rest of the time-series to this reference.

* Subcortical structures are extracted via marching cubes, found in the [https://github.com/BioMedIA/MIRTK MIRTK] package.

* Shapes are decimated and smoothed using [http://www.meshlab.net/ Meshlab].

* A spatiotemporal model was estimated for a single subject with 5 observations using [https://github.com/jamesfishbaugh/shape4D shape4d].

|}

==Illustrations==
Left) Acquisitions of a single subject with observations at 1, 2, 4, 6, and 8 years old. Right) Sub-cortical structures are segmented for each time point.
{|
|-
| [[File:Observations_in_time.gif|500px]] || [[File:8yr_with_labels.png|460px]]
|}

 
Continuous spatiotemporal model of all subcortical structures from 0 to 8 years of age. Color denotes speed in mm/year, with vectors showing direction of growth. Two views of the same growth trajectory is shown below, from the side and from the front.

{|
|-
| http://research.engineering.nyu.edu/~fishbaugh/docs/anim_glyphs_side_small.gif || http://research.engineering.nyu.edu/~fishbaugh/docs/anim_glyphs_front_small.gif
|}

==Background and References==

[1] Fishbaugh, J., Durrleman, S., Prastawa, M., Gerig, G. Geodesic shape regression with multiple geometries and sparse parameters. Medical Image Analysis. Vol 39. pp. 1-17. (2017)

[2] Fishbaugh, J., Durrleman, S., Gerig, G. Estimation of smooth growth trajectories with controlled acceleration from time series shape data. Proc. of Medical Image Computing and Computer Assisted Intervention (MICCAI '11). Vol 6892, pp. 401-408. (2011)

*[http://github.com/Kitware/SlicerSALT SlicerSALT]

Project Week 25/Wrist Kinematics:

2017-06-30T10:08:55Z

Jfishbaugh:

__NOTOC__
Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/james-fishbaugh James Fishbaugh] (NYU Tandon School of Engineering, USA)
*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|
* The clinical goal of this project is the use of dynamic MRI (instead of static series of 3D CT done so far) to explore 3D carpal dynamics to detect kinematic wrist abnormalities in diagnosis/staging/management, to study carpal motion in health/disease, and to evaluate results after surgery
* Collaborators: Catherine N. Petchprapa, MD, NYU Langone Musculoskeletal Imaging & Radiology, and Riccardo Lattanzi, NYU Langone Radiology.
* Challenges for image analysis include segmentation of wrist bones from a longitudinal series of relatively low resolution MRI with anisotropic voxels, consistent segmentation of individual bones across the time series, and joint spatiotemporal modeling of extracted bones as a multi-object complex.

|

* We will use Slicer and various tools for visualization, segmentation, and analysis of the dynamic MR sequence.

|

* Slicer was used to import the DICOM sequence and export 10 individual volumes. The sequences module combined with volume rendering within Slicer provided exploratory qualatative analysis of the wrist kinematics.

* The 10 images in the sequence were used to estimate a template image with [http://stnava.github.io/ANTs/ ANTs].

* 3D segmentation of individual bones was solved via level-set segmentation within [http://www.itksnap.org/pmwiki/pmwiki.php itkSNAP]. Preliminary tests with Slicer GrowCut were not satisfactory due to highly non-isotropic voxels, but more tests will be necessary to explore new advanced features within "Segment Editor".

* Spatiotemporal modeling was performed jointly with all bones using [https://github.com/jamesfishbaugh/shape4D shape4d].

|}

==Illustrations==

Left) One observation of the wrist. Right) A sequence of volume renderings within Slicer to show the 10 observations.

{| class="wikitable"
| [[File:Wrist_multivolume.gif | 550px]] || [[File:Wrist_volume_rendering_small.gif]]
|}

 

Segmentation of the wrist bones in the template space, which are propagated back to the individual observations via mappings computed during template estimation.

[[File:Wrist_template_seg.png]]

 

Continous change trajectories via spatiotemporal modeling. Color denotes speed and vectors denote direction of change.

http://research.engineering.nyu.edu/~fishbaugh/docs/wrist_quick.gif http://research.engineering.nyu.edu/~fishbaugh/docs/wrist_velocity_vectors.gif

==Background and References==
[1] Fishbaugh, J., Durrleman, S., Prastawa, M., Gerig, G. Geodesic shape regression with multiple geometries and sparse parameters. Medical Image Analysis. Vol 39. pp. 1-17. (2017)

[2] Fishbaugh, J., Durrleman, S., Gerig, G. Estimation of smooth growth trajectories with controlled acceleration from time series shape data. Proc. of Medical Image Computing and Computer Assisted Intervention (MICCAI '11). Vol 6892, pp. 401-408. (2011)

Project Week 25/SALT Spatiotemporal Modeling:

2017-06-30T10:06:38Z

Jfishbaugh: /* Background and References */

__NOTOC__

Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/james-fishbaugh James Fishbaugh] (NYU Tandon School of Engineering, USA)
*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|
Slicer Shape AnaLysis Toolbox ([http://github.com/Kitware/SlicerSALT SlicerSALT]) is a standalone version of Slicer specifically for shape analysis, currently under development. One aspect of the project is the validation of the tools to be included in SlicerSALT. In this project, we will begin validating the shape regression component with a longitudinal database of subcortical structures.

The longitudinal infant data has been collected as part of the NIH funded UNC Psychiatry CONTE project to study early brain development in infants at risk for mental illness (PI [https://www.med.unc.edu/psych/earlybraindevresearch/people/principal-investigators/john-gilmore John H. Gilmore]), Distinguished Professor, Director of the Early Brain Development Program, UNC Chapel Hill, Department of Psychiatry).

|

Longitudinal dataset consists of 553 subjects with a total of 1530 scans. MR images are acquired at clustered ages of 1, 2, 4, 6, 8, and 10 years of age. Subjects commonly have missing data points, though many subjects have 3 or more more scans. We focus this week on getting the data organized, setting up the processing pipeline, as well as estimating a model for a single subject. We would also like to identify Slicer tools that are helpful for our analysis, to be considered for inclusion in Slicer SALT.

|

* We found that subject data is not aligned across time; the observations do not share a common coordinate system. For a given set of images, we used Slicer to place a fiducial in each image space. We then used a linear transform to manually move one fiducial onto the other, using the 3D view as a visual guide. The linear transform was used as an initialization for rigid registartion (BRAINS) in Slicer, to compute the final rigid transformation between images. We use the image when the child is oldest as the fixed image, and register the rest of the time-series to this reference.

* Subcortical structures are extracted via marching cubes, found in the [https://github.com/BioMedIA/MIRTK MIRTK] package.

* Shapes are decimated and smoothed using [http://www.meshlab.net/ Meshlab].

* A spatiotemporal model was estimated for a single subject with 5 observations using [https://github.com/jamesfishbaugh/shape4D shape4d].

|}

==Illustrations==
Left) Acquisitions of a single subject with observations at 1, 2, 4, 6, and 8 years old. Right) Sub-cortical structures are segmented for each time point.
{|
|-
| [[File:Observations_in_time.gif|500px]] || [[File:8yr_with_labels.png|460px]]
|}

 
Continuous spatiotemporal model of all subcortical structures from 0 to 8 years of age. Color denotes speed in mm/year, with vectors showing direction of growth. Two views of the same growth trajectory is shown below, from the side and from the front.

{|
|-
| http://research.engineering.nyu.edu/~fishbaugh/docs/anim_glyphs_side_small.gif || http://research.engineering.nyu.edu/~fishbaugh/docs/anim_glyphs_front_small.gif
|}

==Background and References==

[1] Fishbaugh, J., Durrleman, S., Prastawa, M., Gerig, G. Geodesic shape regression with multiple geometries and sparse parameters. Medical Image Analysis. Vol 39. pp. 1-17. (2017)

[2] Fishbaugh, J., Durrleman, S., Gerig, G. Estimation of smooth growth trajectories with controlled acceleration from time series shape data. Proc. of Medical Image Computing and Computer Assisted Intervention (MICCAI '11). Vol 6892, pp. 401-408. (2011)

*[http://github.com/Kitware/SlicerSALT SlicerSALT]

Project Week 25/SALT Spatiotemporal Modeling:

2017-06-30T10:06:18Z

Jfishbaugh:

__NOTOC__

Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/james-fishbaugh James Fishbaugh] (NYU Tandon School of Engineering, USA)
*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|
Slicer Shape AnaLysis Toolbox (SlicerSALT) is a standalone version of Slicer specifically for shape analysis, currently under development. One aspect of the project is the validation of the tools to be included in SlicerSALT. In this project, we will begin validating the shape regression component with a longitudinal database of subcortical structures.

|

Longitudinal dataset consists of 553 subjects with a total of 1530 scans. MR images are acquired at clustered ages of 1, 2, 4, 6, 8, and 10 years of age. Subjects commonly have missing data points, though many subjects have 3 or more more scans. We focus this week on getting the data organized, setting up the processing pipeline, as well as estimating a model for a single subject. We would also like to identify Slicer tools that are helpful for our analysis, to be considered for inclusion in Slicer SALT.

|

* We found that subject data is not aligned across time; the observations do not share a common coordinate system. For a given set of images, we used Slicer to place a fiducial in each image space. We then used a linear transform to manually move one fiducial onto the other, using the 3D view as a visual guide. The linear transform was used as an initialization for rigid registartion (BRAINS) in Slicer, to compute the final rigid transformation between images. We use the image when the child is oldest as the fixed image, and register the rest of the time-series to this reference.

* Subcortical structures are extracted via marching cubes, found in the [https://github.com/BioMedIA/MIRTK MIRTK] package.

* Shapes are decimated and smoothed using [http://www.meshlab.net/ Meshlab].

* A spatiotemporal model was estimated for a single subject with 5 observations using [https://github.com/jamesfishbaugh/shape4D shape4d].

|}

==Illustrations==
Left) Acquisitions of a single subject with observations at 1, 2, 4, 6, and 8 years old. Right) Sub-cortical structures are segmented for each time point.
{|
|-
| [[File:Observations_in_time.gif|500px]] || [[File:8yr_with_labels.png|460px]]
|}

 
Continuous spatiotemporal model of all subcortical structures from 0 to 8 years of age. Color denotes speed in mm/year, with vectors showing direction of growth. Two views of the same growth trajectory is shown below, from the side and from the front.

{|
|-
| http://research.engineering.nyu.edu/~fishbaugh/docs/anim_glyphs_side_small.gif || http://research.engineering.nyu.edu/~fishbaugh/docs/anim_glyphs_front_small.gif
|}

==Background and References==

*[http://github.com/Kitware/SlicerSALT SlicerSALT]

[1] Fishbaugh, J., Durrleman, S., Prastawa, M., Gerig, G. Geodesic shape regression with multiple geometries and sparse parameters. Medical Image Analysis. Vol 39. pp. 1-17. (2017)

[2] Fishbaugh, J., Durrleman, S., Gerig, G. Estimation of smooth growth trajectories with controlled acceleration from time series shape data. Proc. of Medical Image Computing and Computer Assisted Intervention (MICCAI '11). Vol 6892, pp. 401-408. (2011)

Project Week 25/Wrist Kinematics:

2017-06-30T10:05:15Z

Jfishbaugh:

__NOTOC__
Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/james-fishbaugh James Fishbaugh] (NYU Tandon School of Engineering, USA)
*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|
* The clinical goal of this project is the use of dynamic MRI (instead of static series of 3D CT done so far) to explore 3D carpal dynamics to detect kinematic wrist abnormalities in diagnosis/staging/management, to study carpal motion in health/disease, and to evaluate results after surgery
* Collaborators: Catherine N. Petchprapa, MD, NYU Langone Musculoskeletal Imaging & Radiology, and Riccardo Lattanzi, NYU Langone Radiology.
* Challenges for image analysis include segmentation of wrist bones from a longitudinal series of relatively low resolution MRI with anisotropic voxels, consistent segmentation of individual bones across the time series, and joint spatiotemporal modeling of extracted bones as a multi-object complex.

|

We will use Slicer and various tools for visualization, segmentation, and analysis of the dynamic MR sequence.

|

* Slicer was used to import the DICOM sequence and export 10 individual volumes. The sequences module combined with volume rendering within Slicer provided exploratory qualatative analysis of the wrist kinematics.

* The 10 images in the sequence were used to estimate a template image with [http://stnava.github.io/ANTs/ ANTs].

* 3D segmentation of individual bones was solved via level-set segmentation within [http://www.itksnap.org/pmwiki/pmwiki.php itkSNAP]. Preliminary tests with Slicer GrowCut were not satisfactory due to highly non-isotropic voxels, but more tests will be necessary to explore new advanced features within "Segment Editor".

* Spatiotemporal modeling was performed jointly with all bones using [https://github.com/jamesfishbaugh/shape4D shape4d].

|}

==Illustrations==

Left) One observation of the wrist. Right) A sequence of volume renderings within Slicer to show the 10 observations.

{| class="wikitable"
| [[File:Wrist_multivolume.gif | 550px]] || [[File:Wrist_volume_rendering_small.gif]]
|}

 

Segmentation of the wrist bones in the template space, which are propagated back to the individual observations via mappings computed during template estimation.

[[File:Wrist_template_seg.png]]

 

Continous change trajectories via spatiotemporal modeling. Color denotes speed and vectors denote direction of change.

http://research.engineering.nyu.edu/~fishbaugh/docs/wrist_quick.gif http://research.engineering.nyu.edu/~fishbaugh/docs/wrist_velocity_vectors.gif

==Background and References==
[1] Fishbaugh, J., Durrleman, S., Prastawa, M., Gerig, G. Geodesic shape regression with multiple geometries and sparse parameters. Medical Image Analysis. Vol 39. pp. 1-17. (2017)

[2] Fishbaugh, J., Durrleman, S., Gerig, G. Estimation of smooth growth trajectories with controlled acceleration from time series shape data. Proc. of Medical Image Computing and Computer Assisted Intervention (MICCAI '11). Vol 6892, pp. 401-408. (2011)

Project Week 25/Wrist Kinematics:

2017-06-30T10:03:06Z

Jfishbaugh: /* Illustrations */

Project Week 25/Wrist Kinematics:

2017-06-30T10:00:15Z

Jfishbaugh:

Project Week 25/Wrist Kinematics:

2017-06-30T09:58:34Z

Jfishbaugh:

Project Week 25/Wrist Kinematics:

2017-06-30T09:57:50Z

Jfishbaugh:

Project Week 25/Wrist Kinematics:

2017-06-30T09:52:14Z

Jfishbaugh:

Project Week 25/Wrist Kinematics:

2017-06-30T09:45:22Z

Jfishbaugh: /* Illustrations */

Project Week 25/Wrist Kinematics:

2017-06-30T09:45:07Z

Jfishbaugh: /* Illustrations */

Project Week 25/Wrist Kinematics:

2017-06-30T09:44:37Z

Jfishbaugh: /* Illustrations */

Project Week 25/Wrist Kinematics:

2017-06-30T09:44:19Z

Jfishbaugh: /* Illustrations */

Project Week 25/Wrist Kinematics:

2017-06-30T09:43:29Z

Jfishbaugh: /* Illustrations */

File:Wrist volume rendering small.gif

2017-06-30T09:42:18Z

Jfishbaugh:

Project Week 25/Wrist Kinematics:

2017-06-30T09:39:18Z

Jfishbaugh: /* Illustrations */

Project Week 25/Wrist Kinematics:

2017-06-30T09:36:44Z

Jfishbaugh:

Project Week 25/Wrist Kinematics:

2017-06-30T09:36:20Z

Jfishbaugh:

Project Week 25/Wrist Kinematics:

2017-06-30T09:35:16Z

Jfishbaugh:

Project Week 25/Wrist Kinematics:

2017-06-30T09:32:31Z

Jfishbaugh: /* Illustrations */

Project Week 25/Wrist Kinematics:

2017-06-30T09:31:24Z

Jfishbaugh: /* Illustrations */

Project Week 25/Wrist Kinematics:

2017-06-30T09:27:45Z

Jfishbaugh:

Project Week 25/Wrist Kinematics:

2017-06-30T09:27:15Z

Jfishbaugh:

Project Week 25/Wrist Kinematics:

2017-06-30T09:26:40Z

Jfishbaugh:

Project Week 25/Wrist Kinematics:

2017-06-30T09:21:17Z

Jfishbaugh: /* Illustrations */

File:Wrist template seg.png

2017-06-30T09:20:46Z

Jfishbaugh:

Project Week 25/Wrist Kinematics:

2017-06-30T09:18:44Z

Jfishbaugh:

Project Week 25/Wrist Kinematics:

2017-06-30T09:18:11Z

Jfishbaugh:

File:Slicer wrist data vol render.png

2017-06-30T09:16:31Z

Jfishbaugh:

Project Week 25/SALT Spatiotemporal Modeling:

2017-06-30T09:13:04Z

Jfishbaugh: /* Project Description */

__NOTOC__

Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/james-fishbaugh James Fishbaugh] (NYU Tandon School of Engineering, USA)
*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|
Slicer Shape AnaLysis Toolbox (SlicerSALT) is a standalone version of Slicer specifically for shape analysis, currently under development. One aspect of the project is the validation of the tools to be included in SlicerSALT. In this project, we will begin validating the shape regression component with a longitudinal database of subcortical structures.

|

Longitudinal dataset consists of 553 subjects with a total of 1530 scans. MR images are acquired at clustered ages of 1, 2, 4, 6, 8, and 10 years of age. Subjects commonly have missing data points, though many subjects have 3 or more more scans. We focus this week on getting the data organized, setting up the processing pipeline, as well as estimating a model for a single subject. We would also like to identify Slicer tools that are helpful for our analysis, to be considered for inclusion in Slicer SALT.

|

* We found that subject data is not aligned across time; the observations do not share a common coordinate system. For a given set of images, we used Slicer to place a fiducial in each image space. We then used a linear transform to manually move one fiducial onto the other, using the 3D view as a visual guide. The linear transform was used as an initialization for rigid registartion (BRAINS) in Slicer, to compute the final rigid transformation between images. We use the image when the child is oldest as the fixed image, and register the rest of the time-series to this reference.

* Subcortical structures are extracted via marching cubes, found in the [https://github.com/BioMedIA/MIRTK MIRTK] package.

* Shapes are decimated and smoothed using [http://www.meshlab.net/ Meshlab].

* A spatiotemporal model was estimated for a single subject with 5 observations using [https://github.com/jamesfishbaugh/shape4D shape4d].

|}

==Illustrations==
Left) Acquisitions of a single subject with observations at 1, 2, 4, 6, and 8 years old. Right) Sub-cortical structures are segmented for each time point.
{|
|-
| [[File:Observations_in_time.gif|500px]] || [[File:8yr_with_labels.png|460px]]
|}

 
Continuous spatiotemporal model of all subcortical structures from 0 to 8 years of age. Color denotes speed in mm/year, with vectors showing direction of growth. Two views of the same growth trajectory is shown below, from the side and from the front.

{|
|-
| http://research.engineering.nyu.edu/~fishbaugh/docs/anim_glyphs_side_small.gif || http://research.engineering.nyu.edu/~fishbaugh/docs/anim_glyphs_front_small.gif
|}

==Background and References==

*[http://github.com/Kitware/SlicerSALT SlicerSALT]

Project Week 25/SALT Spatiotemporal Modeling:

2017-06-30T09:11:03Z

Jfishbaugh: /* Project Description */

Project Week 25/SALT Spatiotemporal Modeling:

2017-06-30T09:02:24Z

Jfishbaugh: /* Illustrations */

Project Week 25/SALT Spatiotemporal Modeling:

2017-06-30T09:01:21Z

Jfishbaugh: /* Illustrations */

Project Week 25/SALT Spatiotemporal Modeling:

2017-06-30T08:58:43Z

Jfishbaugh: /* Illustrations */

Project Week 25/Multimodal:

2017-06-30T08:55:44Z

Jfishbaugh:

__NOTOC__
Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)
*Sungmin Hong (NYU Tandon School of Engineering, USA)

==Project Description==
* '''Project details can downloaded here as [http://research.engineering.nyu.edu/~fishbaugh/docs/Gerig-Slicer-6-26-2017-final.pptx pptx] or [https://na-mic.org/w/images/a/ae/Gerig-Slicer-6-26-2017-final.pdf pdf].'''
* '''Download a [https://na-mic.org/w/images/a/af/Tutorial_HOA.pdf Slicer tutorial].'''

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|


3D/4D Ophthalmology Image Anaylsis Framework
* Read 4D hyperspectral data
* Viewer and interactor for 3D hi-res image data and 4D hyperspectral data
* Co-registration between 3D hi-res data and 4D hyperspectral data
* Cell segmentation in 3D hi-res data
* Statistics of cells (possibly location, size, distribution)
* Plot of spectra of selected cells or a region-of-interest
|


* Review existing modules in 3D Slicer
** Review existing modules for 4D data viewer, such as, multi-volume viewer extension
** Try existing segmentation modules in Slicer to see if they can work on SIM data
** Review existing modules for cell statistics after segmentation

* Implementation/Integration
** Implement/integrate 4D hyperspectral data viewer to show image and spectral information.
** Integrate registration functionality for co-registration between 3D hi-res data and 4D hyperspectral data at image level
** Integrate user-initialized level set segmentation for cell segmentation or EM segmentation module
** Implement a viewer and an interactor for cell statistics

* User Manual
** Create an user manual to comprehend a overview of an extension
** Guide users to different extensions in a algorithmic flow chart if there are any desired functions (registration, segmentation, or etc.) which are already implemented in existing modules.

|

* Hyperspectral Analysis
** Implemented a module to convert 4D LSM data to a series of 3D data compatible to MultiVolume Explorer
** With a converted series of 3D data, MultiVolume explorer offered a basic analysis tool for hyperspectral data.
** Label statistics or segmentation need to be added in the future

* Registration
** Basic registration algorithms in Slicer worked well on linear registration between 3D SIM and a cropped and dimension reduced 4D LSM data.
** Detecting corresponding regions of 3D SIM in 4D LSM data needs to be developed in the future.

* Segmentation
** Watershed segmentation on 3D hi-res image data (WASP) was not successful.
** Editor/Segmentation Editor worked good on slice-by-slice segmentation
** Will investigate more on 3D segmentation capability of Slicer with possible collaboration with other groups.
|}

==Illustrations==

[[File:SIM-LSM-granules-for-Wiki.png | 900px]]

==Background and References==

Project Week 25/Multimodal:

2017-06-30T08:55:05Z

Jfishbaugh:

__NOTOC__
Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)
*Sungmin Hong (NYU Tandon School of Engineering, USA)

==Project Description==


'''*Project details can downloaded here as [http://research.engineering.nyu.edu/~fishbaugh/docs/Gerig-Slicer-6-26-2017-final.pptx pptx] or [https://na-mic.org/w/images/a/ae/Gerig-Slicer-6-26-2017-final.pdf pdf].'''
'''*Download a [https://na-mic.org/w/images/a/af/Tutorial_HOA.pdf Slicer tutorial].'''

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|


3D/4D Ophthalmology Image Anaylsis Framework
* Read 4D hyperspectral data
* Viewer and interactor for 3D hi-res image data and 4D hyperspectral data
* Co-registration between 3D hi-res data and 4D hyperspectral data
* Cell segmentation in 3D hi-res data
* Statistics of cells (possibly location, size, distribution)
* Plot of spectra of selected cells or a region-of-interest
|


* Review existing modules in 3D Slicer
** Review existing modules for 4D data viewer, such as, multi-volume viewer extension
** Try existing segmentation modules in Slicer to see if they can work on SIM data
** Review existing modules for cell statistics after segmentation

* Implementation/Integration
** Implement/integrate 4D hyperspectral data viewer to show image and spectral information.
** Integrate registration functionality for co-registration between 3D hi-res data and 4D hyperspectral data at image level
** Integrate user-initialized level set segmentation for cell segmentation or EM segmentation module
** Implement a viewer and an interactor for cell statistics

* User Manual
** Create an user manual to comprehend a overview of an extension
** Guide users to different extensions in a algorithmic flow chart if there are any desired functions (registration, segmentation, or etc.) which are already implemented in existing modules.

|

* Hyperspectral Analysis
** Implemented a module to convert 4D LSM data to a series of 3D data compatible to MultiVolume Explorer
** With a converted series of 3D data, MultiVolume explorer offered a basic analysis tool for hyperspectral data.
** Label statistics or segmentation need to be added in the future

* Registration
** Basic registration algorithms in Slicer worked well on linear registration between 3D SIM and a cropped and dimension reduced 4D LSM data.
** Detecting corresponding regions of 3D SIM in 4D LSM data needs to be developed in the future.

* Segmentation
** Watershed segmentation on 3D hi-res image data (WASP) was not successful.
** Editor/Segmentation Editor worked good on slice-by-slice segmentation
** Will investigate more on 3D segmentation capability of Slicer with possible collaboration with other groups.
|}

==Illustrations==

[[File:SIM-LSM-granules-for-Wiki.png | 900px]]

==Background and References==

Project Week 25/Multimodal:

2017-06-30T08:54:32Z

Jfishbaugh:

__NOTOC__
Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)
*Sungmin Hong (NYU Tandon School of Engineering, USA)

==Project Description==

'''*Project details can downloaded here as [http://research.engineering.nyu.edu/~fishbaugh/docs/Gerig-Slicer-6-26-2017-final.pptx pptx] or [https://na-mic.org/w/images/a/ae/Gerig-Slicer-6-26-2017-final.pdf pdf].'''
'''*Download a [https://na-mic.org/w/images/a/af/Tutorial_HOA.pdf Slicer tutorial].'''

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|


3D/4D Ophthalmology Image Anaylsis Framework
* Read 4D hyperspectral data
* Viewer and interactor for 3D hi-res image data and 4D hyperspectral data
* Co-registration between 3D hi-res data and 4D hyperspectral data
* Cell segmentation in 3D hi-res data
* Statistics of cells (possibly location, size, distribution)
* Plot of spectra of selected cells or a region-of-interest
|


* Review existing modules in 3D Slicer
** Review existing modules for 4D data viewer, such as, multi-volume viewer extension
** Try existing segmentation modules in Slicer to see if they can work on SIM data
** Review existing modules for cell statistics after segmentation

* Implementation/Integration
** Implement/integrate 4D hyperspectral data viewer to show image and spectral information.
** Integrate registration functionality for co-registration between 3D hi-res data and 4D hyperspectral data at image level
** Integrate user-initialized level set segmentation for cell segmentation or EM segmentation module
** Implement a viewer and an interactor for cell statistics

* User Manual
** Create an user manual to comprehend a overview of an extension
** Guide users to different extensions in a algorithmic flow chart if there are any desired functions (registration, segmentation, or etc.) which are already implemented in existing modules.

|

* Hyperspectral Analysis
** Implemented a module to convert 4D LSM data to a series of 3D data compatible to MultiVolume Explorer
** With a converted series of 3D data, MultiVolume explorer offered a basic analysis tool for hyperspectral data.
** Label statistics or segmentation need to be added in the future

* Registration
** Basic registration algorithms in Slicer worked well on linear registration between 3D SIM and a cropped and dimension reduced 4D LSM data.
** Detecting corresponding regions of 3D SIM in 4D LSM data needs to be developed in the future.

* Segmentation
** Watershed segmentation on 3D hi-res image data (WASP) was not successful.
** Editor/Segmentation Editor worked good on slice-by-slice segmentation
** Will investigate more on 3D segmentation capability of Slicer with possible collaboration with other groups.
|}

==Illustrations==

[[File:SIM-LSM-granules-for-Wiki.png | 900px]]

==Background and References==

Project Week 25/Multimodal:

2017-06-30T08:52:07Z

Jfishbaugh: /* Project Description */

__NOTOC__
Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)
*Sungmin Hong (NYU Tandon School of Engineering, USA)

==Project Description==

Project details can downloaded here as [http://research.engineering.nyu.edu/~fishbaugh/docs/Gerig-Slicer-6-26-2017-final.pptx pptx] or [https://na-mic.org/w/images/a/ae/Gerig-Slicer-6-26-2017-final.pdf pdf].
Download a [https://na-mic.org/w/images/a/af/Tutorial_HOA.pdf Slicer tutorial].

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|


3D/4D Ophthalmology Image Anaylsis Framework
* Read 4D hyperspectral data
* Viewer and interactor for 3D hi-res image data and 4D hyperspectral data
* Co-registration between 3D hi-res data and 4D hyperspectral data
* Cell segmentation in 3D hi-res data
* Statistics of cells (possibly location, size, distribution)
* Plot of spectra of selected cells or a region-of-interest
|


* Review existing modules in 3D Slicer
** Review existing modules for 4D data viewer, such as, multi-volume viewer extension
** Try existing segmentation modules in Slicer to see if they can work on SIM data
** Review existing modules for cell statistics after segmentation

* Implementation/Integration
** Implement/integrate 4D hyperspectral data viewer to show image and spectral information.
** Integrate registration functionality for co-registration between 3D hi-res data and 4D hyperspectral data at image level
** Integrate user-initialized level set segmentation for cell segmentation or EM segmentation module
** Implement a viewer and an interactor for cell statistics

* User Manual
** Create an user manual to comprehend a overview of an extension
** Guide users to different extensions in a algorithmic flow chart if there are any desired functions (registration, segmentation, or etc.) which are already implemented in existing modules.

|

* Hyperspectral Analysis
** Implemented a module to convert 4D LSM data to a series of 3D data compatible to MultiVolume Explorer
** With a converted series of 3D data, MultiVolume explorer offered a basic analysis tool for hyperspectral data.
** Label statistics or segmentation need to be added in the future

* Registration
** Basic registration algorithms in Slicer worked well on linear registration between 3D SIM and a cropped and dimension reduced 4D LSM data.
** Detecting corresponding regions of 3D SIM in 4D LSM data needs to be developed in the future.

* Segmentation
** Watershed segmentation on 3D hi-res image data (WASP) was not successful.
** Editor/Segmentation Editor worked good on slice-by-slice segmentation
** Will investigate more on 3D segmentation capability of Slicer with possible collaboration with other groups.
|}

==Illustrations==

[[File:SIM-LSM-granules-for-Wiki.png | 900px]]

==Background and References==

Project Week 25/Multimodal:

2017-06-30T08:49:52Z

Jfishbaugh:

__NOTOC__
Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)
*Sungmin Hong (NYU Tandon School of Engineering, USA)

==Project Description==
Project details can downloaded here as [http://research.engineering.nyu.edu/~fishbaugh/docs/Gerig-Slicer-6-26-2017-final.pptx pptx] or [https://na-mic.org/w/images/a/ae/Gerig-Slicer-6-26-2017-final.pdf pdf]

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|


3D/4D Ophthalmology Image Anaylsis Framework
* Read 4D hyperspectral data
* Viewer and interactor for 3D hi-res image data and 4D hyperspectral data
* Co-registration between 3D hi-res data and 4D hyperspectral data
* Cell segmentation in 3D hi-res data
* Statistics of cells (possibly location, size, distribution)
* Plot of spectra of selected cells or a region-of-interest
|


* Review existing modules in 3D Slicer
** Review existing modules for 4D data viewer, such as, multi-volume viewer extension
** Try existing segmentation modules in Slicer to see if they can work on SIM data
** Review existing modules for cell statistics after segmentation

* Implementation/Integration
** Implement/integrate 4D hyperspectral data viewer to show image and spectral information.
** Integrate registration functionality for co-registration between 3D hi-res data and 4D hyperspectral data at image level
** Integrate user-initialized level set segmentation for cell segmentation or EM segmentation module
** Implement a viewer and an interactor for cell statistics

* User Manual
** Create an user manual to comprehend a overview of an extension
** Guide users to different extensions in a algorithmic flow chart if there are any desired functions (registration, segmentation, or etc.) which are already implemented in existing modules.

|

* Hyperspectral Analysis
** Implemented a module to convert 4D LSM data to a series of 3D data compatible to MultiVolume Explorer
** With a converted series of 3D data, MultiVolume explorer offered a basic analysis tool for hyperspectral data.
** Label statistics or segmentation need to be added in the future

* Registration
** Basic registration algorithms in Slicer worked well on linear registration between 3D SIM and a cropped and dimension reduced 4D LSM data.
** Detecting corresponding regions of 3D SIM in 4D LSM data needs to be developed in the future.

* Segmentation
** Watershed segmentation on 3D hi-res image data (WASP) was not successful.
** Editor/Segmentation Editor worked good on slice-by-slice segmentation
** Will investigate more on 3D segmentation capability of Slicer with possible collaboration with other groups.
|}

==Illustrations==

[[File:SIM-LSM-granules-for-Wiki.png | 900px]]

==Background and References==

Project Week 25/Multimodal:

2017-06-30T08:49:15Z

Jfishbaugh: /* Project Description */

__NOTOC__
Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)
*Sungmin Hong (NYU Tandon School of Engineering, USA)

==Project Description==
Project details can downloaded here as [http://research.engineering.nyu.edu/~fishbaugh/docs/Gerig-Slicer-6-26-2017-final.pptx pptx] or [File:File:Gerig-Slicer-6-26-2017-final.pdf | pdf]

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|


3D/4D Ophthalmology Image Anaylsis Framework
* Read 4D hyperspectral data
* Viewer and interactor for 3D hi-res image data and 4D hyperspectral data
* Co-registration between 3D hi-res data and 4D hyperspectral data
* Cell segmentation in 3D hi-res data
* Statistics of cells (possibly location, size, distribution)
* Plot of spectra of selected cells or a region-of-interest
|


* Review existing modules in 3D Slicer
** Review existing modules for 4D data viewer, such as, multi-volume viewer extension
** Try existing segmentation modules in Slicer to see if they can work on SIM data
** Review existing modules for cell statistics after segmentation

* Implementation/Integration
** Implement/integrate 4D hyperspectral data viewer to show image and spectral information.
** Integrate registration functionality for co-registration between 3D hi-res data and 4D hyperspectral data at image level
** Integrate user-initialized level set segmentation for cell segmentation or EM segmentation module
** Implement a viewer and an interactor for cell statistics

* User Manual
** Create an user manual to comprehend a overview of an extension
** Guide users to different extensions in a algorithmic flow chart if there are any desired functions (registration, segmentation, or etc.) which are already implemented in existing modules.

|

* Hyperspectral Analysis
** Implemented a module to convert 4D LSM data to a series of 3D data compatible to MultiVolume Explorer
** With a converted series of 3D data, MultiVolume explorer offered a basic analysis tool for hyperspectral data.
** Label statistics or segmentation need to be added in the future

* Registration
** Basic registration algorithms in Slicer worked well on linear registration between 3D SIM and a cropped and dimension reduced 4D LSM data.
** Detecting corresponding regions of 3D SIM in 4D LSM data needs to be developed in the future.

* Segmentation
** Watershed segmentation on 3D hi-res image data (WASP) was not successful.
** Editor/Segmentation Editor worked good on slice-by-slice segmentation
** Will investigate more on 3D segmentation capability of Slicer with possible collaboration with other groups.
|}

==Illustrations==

[[File:SIM-LSM-granules-for-Wiki.png | 900px]]

==Background and References==

Project Week 25/Multimodal:

2017-06-30T08:49:00Z

Jfishbaugh: /* Project Description */

__NOTOC__
Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)
*Sungmin Hong (NYU Tandon School of Engineering, USA)

==Project Description==
Project details can downloaded here as [http://research.engineering.nyu.edu/~fishbaugh/docs/Gerig-Slicer-6-26-2017-final.pptx pptx] or [File:File:Gerig-Slicer-6-26-2017-final.pdf pdf]

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|


3D/4D Ophthalmology Image Anaylsis Framework
* Read 4D hyperspectral data
* Viewer and interactor for 3D hi-res image data and 4D hyperspectral data
* Co-registration between 3D hi-res data and 4D hyperspectral data
* Cell segmentation in 3D hi-res data
* Statistics of cells (possibly location, size, distribution)
* Plot of spectra of selected cells or a region-of-interest
|


* Review existing modules in 3D Slicer
** Review existing modules for 4D data viewer, such as, multi-volume viewer extension
** Try existing segmentation modules in Slicer to see if they can work on SIM data
** Review existing modules for cell statistics after segmentation

* Implementation/Integration
** Implement/integrate 4D hyperspectral data viewer to show image and spectral information.
** Integrate registration functionality for co-registration between 3D hi-res data and 4D hyperspectral data at image level
** Integrate user-initialized level set segmentation for cell segmentation or EM segmentation module
** Implement a viewer and an interactor for cell statistics

* User Manual
** Create an user manual to comprehend a overview of an extension
** Guide users to different extensions in a algorithmic flow chart if there are any desired functions (registration, segmentation, or etc.) which are already implemented in existing modules.

|

* Hyperspectral Analysis
** Implemented a module to convert 4D LSM data to a series of 3D data compatible to MultiVolume Explorer
** With a converted series of 3D data, MultiVolume explorer offered a basic analysis tool for hyperspectral data.
** Label statistics or segmentation need to be added in the future

* Registration
** Basic registration algorithms in Slicer worked well on linear registration between 3D SIM and a cropped and dimension reduced 4D LSM data.
** Detecting corresponding regions of 3D SIM in 4D LSM data needs to be developed in the future.

* Segmentation
** Watershed segmentation on 3D hi-res image data (WASP) was not successful.
** Editor/Segmentation Editor worked good on slice-by-slice segmentation
** Will investigate more on 3D segmentation capability of Slicer with possible collaboration with other groups.
|}

==Illustrations==

[[File:SIM-LSM-granules-for-Wiki.png | 900px]]

==Background and References==

File:Gerig-Slicer-6-26-2017-final.pdf

2017-06-30T08:43:39Z

Jfishbaugh:

Project Week 25/Multimodal:

2017-06-30T08:41:28Z

Jfishbaugh:

__NOTOC__
Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)
*Sungmin Hong (NYU Tandon School of Engineering, USA)

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|


3D/4D Ophthalmology Image Anaylsis Framework
* Read 4D hyperspectral data
* Viewer and interactor for 3D hi-res image data and 4D hyperspectral data
* Co-registration between 3D hi-res data and 4D hyperspectral data
* Cell segmentation in 3D hi-res data
* Statistics of cells (possibly location, size, distribution)
* Plot of spectra of selected cells or a region-of-interest
|


* Review existing modules in 3D Slicer
** Review existing modules for 4D data viewer, such as, multi-volume viewer extension
** Try existing segmentation modules in Slicer to see if they can work on SIM data
** Review existing modules for cell statistics after segmentation

* Implementation/Integration
** Implement/integrate 4D hyperspectral data viewer to show image and spectral information.
** Integrate registration functionality for co-registration between 3D hi-res data and 4D hyperspectral data at image level
** Integrate user-initialized level set segmentation for cell segmentation or EM segmentation module
** Implement a viewer and an interactor for cell statistics

* User Manual
** Create an user manual to comprehend a overview of an extension
** Guide users to different extensions in a algorithmic flow chart if there are any desired functions (registration, segmentation, or etc.) which are already implemented in existing modules.

|

* Hyperspectral Analysis
** Implemented a module to convert 4D LSM data to a series of 3D data compatible to MultiVolume Explorer
** With a converted series of 3D data, MultiVolume explorer offered a basic analysis tool for hyperspectral data.
** Label statistics or segmentation need to be added in the future

* Registration
** Basic registration algorithms in Slicer worked well on linear registration between 3D SIM and a cropped and dimension reduced 4D LSM data.
** Detecting corresponding regions of 3D SIM in 4D LSM data needs to be developed in the future.

* Segmentation
** Watershed segmentation on 3D hi-res image data (WASP) was not successful.
** Editor/Segmentation Editor worked good on slice-by-slice segmentation
** Will investigate more on 3D segmentation capability of Slicer with possible collaboration with other groups.
|}

==Illustrations==

[[File:SIM-LSM-granules-for-Wiki.png | 900px]]

==Background and References==

Project Week 25/Multimodal:

2017-06-30T08:40:55Z

Jfishbaugh:

__NOTOC__
Back to [[Project_Week_25#Projects|Projects List]]

==Key Investigators==

*[http://engineering.nyu.edu/people/guido-gerig Guido Gerig] (NYU Tandon School of Engineering, USA)
*Sungmin Hong (NYU Tandon School of Engineering, USA)

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|


3D/4D Ophthalmology Image Anaylsis Framework
* Read 4D hyperspectral data
* Viewer and interactor for 3D hi-res image data and 4D hyperspectral data
* Co-registration between 3D hi-res data and 4D hyperspectral data
* Cell segmentation in 3D hi-res data
* Statistics of cells (possibly location, size, distribution)
* Plot of spectra of selected cells or a region-of-interest
|


* Review existing modules in 3D Slicer
** Review existing modules for 4D data viewer, such as, multi-volume viewer extension
** Try existing segmentation modules in Slicer to see if they can work on SIM data
** Review existing modules for cell statistics after segmentation

* Implementation/Integration
** Implement/integrate 4D hyperspectral data viewer to show image and spectral information.
** Integrate registration functionality for co-registration between 3D hi-res data and 4D hyperspectral data at image level
** Integrate user-initialized level set segmentation for cell segmentation or EM segmentation module
** Implement a viewer and an interactor for cell statistics

* User Manual
** Create an user manual to comprehend a overview of an extension
** Guide users to different extensions in a algorithmic flow chart if there are any desired functions (registration, segmentation, or etc.) which are already implemented in existing modules.

|

* Hyperspectral Analysis
** Implemented a module to convert 4D LSM data to a series of 3D data compatible to MultiVolume Explorer
** With a converted series of 3D data, MultiVolume explorer offered a basic analysis tool for hyperspectral data.
** Label statistics or segmentation need to be added in the future

* Registration
** Basic registration algorithms in Slicer worked well on linear registration between 3D SIM and a cropped and dimension reduced 4D LSM data.
** Detecting corresponding regions of 3D SIM in 4D LSM data needs to be developed in the future.

* Segmentation
** Watershed segmentation on 3D hi-res image data (WASP) was not successful.
** Editor/Segmentation Editor worked good on slice-by-slice segmentation
** Will investigate more on 3D segmentation capability of Slicer with possible collaboration with other groups.
|}

==Illustrations==

[[File:SIM-LSM-granules-for-Wiki.png]]

==Background and References==

File:SIM-LSM-granules-for-Wiki.png

2017-06-30T08:40:28Z

Jfishbaugh:

Project Week 25/SALT Spatiotemporal Modeling:

2017-06-30T08:39:45Z

Jfishbaugh:

Project Week 25/SALT Spatiotemporal Modeling:

2017-06-30T08:39:19Z

Jfishbaugh: /* Project Description */