NAMIC Wiki - User contributions [en]

2017 Winter Project Week/HyperspectralOpht

2017-01-13T16:05:58Z

Shong: /* Acknowledgement */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|4D hyperspectral data
File:SIM_LSM_ROI.png|link=File:SIM_LSM_ROI.png|3D hrSIM image data and 4D LSM data
File:Segmentation_Cells.png|link=File:Segmentation_Cells.png|Cell segmentation on 3D SIM data. The segmentation mask is overlayed on 4D LSM data for spectral analysis.
File:LSM_SIM_Zoomed.png|link=File:LSM_SIM_Zoomed.png|Individual granules (Lipofuscin (bright) and Melano-lipofuscin (dark)) in a zoomed view of LSM and SIM.
File:MultiVolExp Zoomed Plot.png|link=File:MultiVolExp Zoomed Plot.png|Hyperspectral Information of pre-segmented individual granule (LF).
File:Tutorial HOA.pdf|link=File:Tutorial HOA.pdf|Tutorial on Hyperspectral Analysis with 3D Slicer

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each granule. The examination of spectral characteristics and statistics of granules, related with age and disease progress might reveal metabolism in human retina physiology.

{| 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.

|}

==Background and References==


==Acknowledgement==
We would like to thank Dr. Thomas Ach (University Hospital Würzburg, Germany) for the significant contribution on the data and the project.

2017 Winter Project Week/HyperspectralOpht

2017-01-13T16:03:02Z

Shong:

File:Tutorial HOA.pdf

2017-01-13T16:00:57Z

Shong: Tutorial for Hyperspectral Analysis using Slicer

Tutorial for Hyperspectral Analysis using Slicer

2017 Winter Project Week/HyperspectralOpht

2017-01-13T15:24:05Z

Shong:

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|4D hyperspectral data
File:SIM_LSM_ROI.png|link=File:SIM_LSM_ROI.png|3D hrSIM image data and 4D LSM data
File:Segmentation_Cells.png|link=File:Segmentation_Cells.png|Cell segmentation on 3D SIM data. The segmentation mask is overlayed on 4D LSM data for spectral analysis.
File:LSM_SIM_Zoomed.png|link=File:LSM_SIM_Zoomed.png|Individual granules (Lipofuscin (bright) and Melano-lipofuscin (dark)) in a zoomed view of LSM and SIM.
File:MultiVolExp Zoomed Plot.png|link=File:MultiVolExp Zoomed Plot.png|Hyperspectral Information of pre-segmented individual granule (LF).

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each granule. The examination of spectral characteristics and statistics of granules, related with age and disease progress might reveal metabolism in human retina physiology.

{| 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.

|}

==Background and References==


==Acknowledgement==
We would like to thank Dr. Thomas Ach (University Hospital Würzburg, Germany) for the contribution on the data and the project.

File:MultiVolExp Zoomed Plot.png

2017-01-13T15:22:23Z

Shong: The plot of hyperspectral information of a region with pre-segmented region (blue) on MultiVolume Explorer.

The plot of hyperspectral information of a region with pre-segmented region (blue) on MultiVolume Explorer.

2017 Winter Project Week/HyperspectralOpht

2017-01-13T14:41:08Z

Shong: /* Project Description */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|4D hyperspectral data
File:SIM_LSM_ROI.png|link=File:SIM_LSM_ROI.png|3D hrSIM image data and 4D LSM data
File:Segmentation_Cells.png|link=File:Segmentation_Cells.png|Cell segmentation on 3D SIM data. The segmentation mask is overlayed on 4D LSM data for spectral analysis.
File:LSM_SIM_Zoomed.png|link=File:LSM_SIM_Zoomed.png|Individual granules (Lipofuscin (bright) and Melano-lipofuscin (dark)) in a zoomed view of LSM and SIM.

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each granule. The examination of spectral characteristics and statistics of granules, related with age and disease progress might reveal metabolism in human retina physiology.

{| 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.

|}

==Background and References==


==Acknowledgement==
We would like to thank Dr. Thomas Ach (University Hospital Würzburg, Germany) for the contribution on the data and the project.

2017 Winter Project Week/HyperspectralOpht

2017-01-13T14:40:14Z

Shong: /* Project Description */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|4D hyperspectral data
File:SIM_LSM_ROI.png|link=File:SIM_LSM_ROI.png|3D hrSIM image data and 4D LSM data
File:Segmentation_Cells.png|link=File:Segmentation_Cells.png|Cell segmentation on 3D SIM data. The segmentation mask is overlayed on 4D LSM data for spectral analysis.
File:LSM_SIM_Zoomed.png|link=File:LSM_SIM_Zoomed.png|Individual granules (Lipofuscin (bright) and Melano-lipofuscin (dark)) in a zoomed view of LSM and SIM.

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each granule. The examination of spectral characteristics and statistics of granules, related with age and disease progress might reveal metabolism in human retina physiology.

{| 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

*** Need to convert the 4D data recognizable to Multivolume importer (A series of 3D data)

* 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.

|}

==Background and References==


==Acknowledgement==
We would like to thank Dr. Thomas Ach (University Hospital Würzburg, Germany) for the contribution on the data and the project.

2017 Winter Project Week/HyperspectralOpht

2017-01-12T19:36:03Z

Shong: /* Project Description */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|4D hyperspectral data
File:SIM_LSM_ROI.png|link=File:SIM_LSM_ROI.png|3D hrSIM image data and 4D LSM data
File:Segmentation_Cells.png|link=File:Segmentation_Cells.png|Cell segmentation on 3D SIM data. The segmentation mask is overlayed on 4D LSM data for spectral analysis.
File:LSM_SIM_Zoomed.png|link=File:LSM_SIM_Zoomed.png|Individual granules (Lipofuscin (bright) and Melano-lipofuscin (dark)) in a zoomed view of LSM and SIM.

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each granule. The examination of spectral characteristics and statistics of granules, related with age and disease progress might reveal metabolism in human retina physiology.

{| 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
** Investigated Multivolume Explorer to analyze hyperspectral data
*** Need to convert the 4D data recognizable to Multivolume importer (A series of 3D data)

* Segmentation
** Watershed segmentation on 3D hi-res image data (WASP) was not successful.
** Editor/Segmentation Editor worked good on slice-by-slice segmentation
** Need to explore 3D segmentation capability of Editor/Segmentation Editor

** Moved onto hyperspectral analysis with manual annotation

|}

==Background and References==


==Acknowledgement==
We would like to thank Dr. Thomas Ach (University Hospital Würzburg, Germany) for the contribution on the data and the project.

2017 Winter Project Week/HyperspectralOpht

2017-01-10T21:21:01Z

Shong: /* Project Description */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|4D hyperspectral data
File:SIM_LSM_ROI.png|link=File:SIM_LSM_ROI.png|3D hrSIM image data and 4D LSM data
File:Segmentation_Cells.png|link=File:Segmentation_Cells.png|Cell segmentation on 3D SIM data. The segmentation mask is overlayed on 4D LSM data for spectral analysis.
File:LSM_SIM_Zoomed.png|link=File:LSM_SIM_Zoomed.png|Individual granules (Lipofuscin (bright) and Melano-lipofuscin (dark)) in a zoomed view of LSM and SIM.

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each granule. The examination of spectral characteristics and statistics of granules, related with age and disease progress might reveal metabolism in human retina physiology.

{| 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.

|

* Segmentation
** Watershed segmentation on 3D hi-res image data (WASP) was not successful.
** Moved onto hyperspectral analysis with manual annotation

* Hyperspectral Analysis
** Investigated Multivolume Explorer to analyze hyperspectral data
*** Need to convert the 4D data recognizable to Multivolume importer (A series of 3D data)
|}

==Background and References==


==Acknowledgement==
We would like to thank Dr. Thomas Ach (University Hospital Würzburg, Germany) for the contribution on the data and the project.

2017 Winter Project Week/HyperspectralOpht

2017-01-10T21:11:38Z

Shong:

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|4D hyperspectral data
File:SIM_LSM_ROI.png|link=File:SIM_LSM_ROI.png|3D hrSIM image data and 4D LSM data
File:Segmentation_Cells.png|link=File:Segmentation_Cells.png|Cell segmentation on 3D SIM data. The segmentation mask is overlayed on 4D LSM data for spectral analysis.
File:LSM_SIM_Zoomed.png|link=File:LSM_SIM_Zoomed.png|Individual granules (Lipofuscin (bright) and Melano-lipofuscin (dark)) in a zoomed view of LSM and SIM.

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each granule. The examination of spectral characteristics and statistics of granules, related with age and disease progress might reveal metabolism in human retina physiology.

{| 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.

|

*
|}

==Background and References==


==Acknowledgement==
We would like to thank Dr. Thomas Ach (University Hospital Würzburg, Germany) for the contribution on the data and the project.

2017 Winter Project Week/HyperspectralOpht

2017-01-10T21:10:52Z

Shong:

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|4D hyperspectral data
File:SIM_LSM_ROI.png|link=File:SIM_LSM_ROI.png|3D hrSIM image data and 4D LSM data
File:Segmentation_Cells.png|link=File:Segmentation_Cells.png|Cell segmentation on 3D SIM data. The segmentation mask is overlayed on 4D LSM data for spectral analysis.
File:LSM_SIM_Zoomed.png|link=File:LSM_SIM_Zoomed.png|Individual granules (Lipofuscin (bright) and Melano-lipofuscin (dark)) in a zoomed view of LSM and SIM.

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each granule. The examination of spectral characteristics and statistics of granules, related with age and disease progress might reveal metabolism in human retina physiology.

{| 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.

|

*
|}

==Background and References==


==Acknowledgement==
We would like to thank Dr. Thomas Ach (MD, University of Würzburg) for the contribution on the data and the project.

File:SIM LSM ROI.png

2017-01-09T17:03:13Z

Shong:

3D High resolution image data (hrSIM, High-resolution structured illumination microscopy, Zeiss Elyra.S1) and 4D Hyperspectral data (LSM) with spectral information. hrSIM image data can be co-registered to a subregion of 4D LSM data.
Figure Courtesy of Prof. Guido Gerig, NYU

2017 Winter Project Week/HyperspectralOpht

2017-01-09T16:23:09Z

Shong: /* Project Description */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|4D hyperspectral data
File:SIM_LSM_ROI.png|link=File:SIM_LSM_ROI.png|3D hrSIM image data and 4D LSM data
File:Segmentation_Cells.png|link=File:Segmentation_Cells.png|Cell segmentation on 3D SIM data. The segmentation mask is overlayed on 4D LSM data for spectral analysis.
File:LSM_SIM_Zoomed.png|link=File:LSM_SIM_Zoomed.png|Individual granules (Lipofuscin (bright) and Melano-lipofuscin (dark)) in a zoomed view of LSM and SIM.

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each granule. The examination of spectral characteristics and statistics of granules, related with age and disease progress might reveal metabolism in human retina physiology.

{| 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.

|

*
|}

==Background and References==

2017 Winter Project Week/HyperspectralOpht

2017-01-04T15:08:26Z

Shong:

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|4D hyperspectral data
File:SIM_LSM_ROI.png|link=File:SIM_LSM_ROI.png|3D hrSIM image data and 4D LSM data
File:Segmentation_Cells.png|link=File:Segmentation_Cells.png|Cell segmentation on 3D SIM data. The segmentation mask is overlayed on 4D LSM data for spectral analysis.
File:LSM_SIM_Zoomed.png|link=File:LSM_SIM_Zoomed.png|Individual granules (Lipofuscin (bright) and Melano-lipofuscin (dark)) in a zoomed view of LSM and SIM.

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each cell.

{| 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.

|

*
|}

==Background and References==

File:LSM SIM Zoomed.png

2017-01-04T15:05:25Z

Shong: Individual granules (Lipofuscin and Melano-lipofuscin) in a zoomed view of LSM and SIM.

Individual granules (Lipofuscin and Melano-lipofuscin) in a zoomed view of LSM and SIM.

2017 Winter Project Week/HyperspectralOpht

2017-01-03T23:25:07Z

Shong:

File:Segmentation Cells.png

2017-01-03T23:23:09Z

Shong: Segmentation of lipofuscin granules in 3D SIM data. Segmentation label is mapped on 4D LSM data for spectral analysis

Segmentation of lipofuscin granules in 3D SIM data.
Segmentation label is mapped on 4D LSM data for spectral analysis

2017 Winter Project Week/HyperspectralOpht

2017-01-03T22:48:28Z

Shong:

2017 Winter Project Week/HyperspectralOpht

2017-01-03T22:48:05Z

Shong:

File:SIM LSM ROI.png

2017-01-03T22:46:52Z

Shong:

3D High resolution image data (hrSIM, High-resolution structured illumination microscopy, Zeis Elyra.S1) and 4D Hyperspectral data (LSM) with spectral information. hrSIM image data can be co-registered to a subregion of 4D LSM data.
Figure Courtesy of Prof. Guido Gerig, NYU

File:SIM LSM ROI.png

2017-01-03T22:46:29Z

Shong: 3D High resolution image data (hrSIM, High-resolution structured illumination microscopy, Zeis Elyra.S1) and 4D Hyperspectral data (LSM) with spectral information. hrSIM image data can be co-registered to a subregion of 4D LSM data. Figure Courtesy of...

2017 Winter Project Week/HyperspectralOpht

2017-01-03T22:00:39Z

Shong:

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|Description of 4D Hyperspectral Data

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each cell.

{| 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.

|

*
|}

==Background and References==

2017 Winter Project Week/HyperspectralOpht

2017-01-03T22:00:07Z

Shong:

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|[Description of 4D Hyperspectral Data]

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each cell.

{| 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.

|

*
|}

==Background and References==

2017 Winter Project Week/HyperspectralOpht

2017-01-03T21:59:35Z

Shong:

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|[[File:4DLSMData.png|Description of 4D Hyperspectral Data]]

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each cell.

{| 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.

|

*
|}

==Background and References==

2017 Winter Project Week/HyperspectralOpht

2017-01-03T21:59:13Z

Shong:

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
File:4DLSMData.png|link=File:4DLSMData.png|[File:4DLSMData.png|Description of 4D Hyperspectral Data]

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each cell.

{| 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.

|

*
|}

==Background and References==

File:4DLSMData.png

2017-01-03T21:57:13Z

Shong: Description of 4D Hyperspectral Data captured by confocal multispectral laser scanning microscopy (cmLSM, Zeiss LSM780). The data has 24 channels of emission spectra (wavelength : 490-695nm) for each voxel of 3D image data (224.8 x 224.8 micrometer^2,...

Description of 4D Hyperspectral Data captured by confocal multispectral laser scanning microscopy (cmLSM, Zeiss LSM780).
The data has 24 channels of emission spectra (wavelength : 490-695nm) for each voxel of 3D image data (224.8 x 224.8 micrometer^2, Spacing : 390 nm).

2017 Winter Project Week/HyperspectralOpht

2017-01-03T21:51:19Z

Shong: /* Project Description */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each cell.

{| 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.

|

*
|}

==Background and References==

2017 Winter Project Week/HyperspectralOpht

2017-01-03T21:51:18Z

Shong: /* Project Description */

2017 Winter Project Week/HyperspectralOpht

2017-01-03T21:51:17Z

Shong: /* Project Description */

2017 Winter Project Week/HyperspectralOpht

2017-01-03T20:46:16Z

Shong:

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==

* Sungmin Hong (New York University)
* Guido Gerig (New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each cell.

{| 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.

|

*
|}

==Background and References==

2017 Winter Project Week/HyperspectralOpht

2017-01-03T05:42:45Z

Shong: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==

* Sungmin Hong (CSE, Tandon School of Engineering, New York University)
* Guido Gerig (CSE, Tandon School of Engineering, New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each cell.

{| 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.

|

*
|}

==Background and References==

2017 Winter Project Week/HyperspectralOpht

2017-01-03T05:42:23Z

Shong: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==

* Sungmin Hong (Tandon School of Engineering, New York University)
* Guido Gerig (Tandon School of Engineering, New York University)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each cell.

{| 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.

|

*
|}

==Background and References==

2017 Winter Project Week/HyperspectralOpht

2017-01-03T05:41:27Z

Shong: /* Project Description */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==

* Sungmin Hong (NYU)
* Guido Gerig (NYU)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each cell.

{| 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.

|

*
|}

==Background and References==

2017 Winter Project Week/HyperspectralOpht

2017-01-03T05:35:02Z

Shong: /* Project Description */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==

* Sungmin Hong (NYU)
* Guido Gerig (NYU)

==Project Description==

This project aims to offer a tool which makes use of 3D/4D ophthalmology data in different modalities to extract information which can compensate each other for richer analysis.
3D high resolution data (high resolution structured illumination microscopy, SIM) displays individual cells with sharp boundaries which are hard to be localized in 4D hyperspectral data (confocal multispectral laser scanning microscopy, LSM) because of low resolution.
The tool that we want to provide to users should offer image processing modules, such as, co-registration between SIM and LSM data, segmentation on SIM and mapping the segmentation label from SIM to LSM to analyze spectral information of each cell.

{| 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
**
|

*
|}

==Background and References==

2017 Winter Project Week/HyperspectralOpht

2017-01-03T05:22:42Z

Shong: /* Project Description */

2017 Winter Project Week/HyperspectralOpht

2017-01-03T05:22:24Z

Shong: /* Key Investigators */

2017 Winter Project Week/HyperspectralOpht

2017-01-03T05:20:22Z

Shong: 3D/4D hyperspectral data analysis

2017 Winter Project Week/HyperspectralOpht

2016-12-14T22:40:02Z

Shong: /* Key Investigators */

2017 Winter Project Week/HyperspectralOpht

2016-12-13T20:43:08Z

Shong: /* Key Investigators */

2017 Winter Project Week

2016-12-13T20:27:48Z

Shong: /* To be Categorized */

__NOTOC__
[[image:PW-Winter2017.png|300px]]

=Welcome to the web page for the 24th Project Week!=

The 24th NA-MIC Project Week open source hackathon is being held during the week of January 9-13, 2017 at MIT. Please go through this page for information, and if you have questions, please contact [https://www.spl.harvard.edu/pages/People/tkapur Tina Kapur, PhD].

==Logistics==

*'''Dates:''' January 9-13, 2017.
*'''Location:''' [https://www.google.com/maps/place/MIT:+Computer+Science+and+Artificial+Intelligence+Laboratory/@42.361864,-71.090563,16z/data=!4m2!3m1!1s0x0:0x303ada1e9664dfed?hl=en MIT CSAIL], Cambridge, MA. (Rooms: [[MIT_Project_Week_Rooms#Kiva|Kiva]], R&D)
*'''REGISTRATION:''' Register [https://www.regonline.com/2017projectweek here]. Registration Fee: $330.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.

== Introduction ==
The National Alliance for Medical Image Computing (NAMIC), was founded in 2005 and chartered with building a computational infrastructure to support biomedical research as part of the NIH funded [http://www.ncbcs.org/ NCBC] program. The work of this alliance has resulted in important progress in algorithmic research, an open source medical image computing platform [http://www.slicer.org 3D Slicer], enhancements to the underlying building blocks [http://www.vtk.org VTK], [http://www.itk.org ITK], [http://www.cmake.org CMake], and [http://www.cdash.org CDash], and the creation of a community of algorithm researchers, biomedical scientists and software engineers who are committed to open science. This community meets twice a year in an open source hackathon event called Project Week.

[[Engineering:Programming_Events|Project Week]] is a semi-annual open source hackathon which draws 60-120 researchers. As of August 2014, it is a [http://www.miccai.org/organization MICCAI] endorsed event. The participants work collaboratively on open-science solutions for problems that lie on the interfaces of the fields of computer science, mechanical engineering, biomedical engineering, and medicine. In contrast to conventional conferences and workshops the primary focus of the Project Weeks is to make progress in projects (as opposed to reporting about progress). The objective of the Project Weeks is to provide a venue for this community of medical open source software creators. Project Weeks are open to all, are publicly advertised, and are funded through fees paid by the attendees. Participants are encouraged to stay for the entire event.

Project Week activities: Everyone shows up with a project. Some people are working on the platform. Some people are developing algorithms. Some people are applying the tools to their research problems. We begin the week by introducing projects and connecting teams. We end the week by reporting progress. In addition to the ongoing working sessions, breakout sessions are organized ad-hoc on a variety of special topics. These topics include: discussions of software architecture, presentations of new features and approaches and topics such as Image-Guided Therapy.

Several funded projects use the Project Week as a place to convene and collaborate. These include [http://nac.spl.harvard.edu/ NAC], [http://www.ncigt.org/ NCIGT], [http://qiicr.org/ QIICR], and [http://ocairo.technainstitute.com/open-source-software-platforms-and-databases-for-the-adaptive-process/ OCAIRO].

A summary of all previous Project Events is available [[Project_Events#Past_Project_Weeks|here]].

This project week is an event [[Post-NCBC-2014|endorsed]] by the MICCAI society.

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

==Conference Calls for Preparation==

Domestic conference call number: 800-501-8979 
Access code: 7327389 

[[media:TollFree.png|International conference call numbers]] 
Access code: 7327389 

Weekly, Tuesdays 10am, Boston time from November 22nd, 2016 to January 3rd, 2017.

===Tcon #4: December 6 ===
*10am Boston time
*Project Discussions with focus on Machine Learning and Web-based software for clinical research/trials

Participants
* Tina Kapur (MIT) - organizer
* Steve Pieper (Isomics/BWH)
* Beatriz, JC (Kitware)

===Tcon #3: November 29 ===
*10am Boston time
*Project Discussions with focus on Machine Learning and Web-based software for clinical research/trials
Participants
* Tina Kapur (MIT) - organizer; planning Nvidia webinars. Proposal to use Monday as a schedule for the deep learning workshop before workshop begins.
*Steve Pieper (Isomics/BWH) - NVidia update; met with Abdul at NVidia (Medical applications of deep learning). Positive meeting and Slicer might be a reference platform. NVIdia will host a Monday Afternoon session at Project Week and hold outside webinars for basic deep learning information. Considering two or three days of a few hours each for webinar. Discussion of a prize board awarded for demonstrations that Nvidia could use.
*Patmal (Univ. of Pennsylvania Biomedical Group) Showcase some of their capability: cancer & phenomics toolkit; facilitate integration from their toolkit to/from Slicer; They have a few applications that might be interesting to others during project week - (1) fibre tracking in brain, (2) lung tumor segmentation, (3) density estimation
*Beatriz, Alexis, and JC (Kitware) - Slicer support and 3 other projects (1) customized childrern's medical, (1) Alex submitted volumetric meshing project in Slicer idea to the NA-MIC email list; have a discussion on Meshing; the goal is for Finite Element analsis and light imaging (2), better MATLAB integration (use Python-like interface). (3) Beatrix - shape analysis algorithms to be added to Slicer based on recently-funded grant.
*JC (Kitware) - integration of iPython and Slicer; Tina Suggested also Deep Infer framework to integrate with Slicer
*Louise Oram (Stockholm) - Specialization of Slicer for Liver surgery, possibly using SliceLet approach or other way to tailor the Slicer UI. Currently customizations need to be in the Extension catalog. Also interested in Liver vessel segmentation project and other Liver segmentation tasks. This might complement with MeVIS lab capability focused on liver. Separate modules for liver surgery that might be integrated together later.
*Frank Pricework (BWH) - General GPU programming; experiment with rehosting algorithms has has previously developed to GPU.
*Paolo, Italy - (Summer Project Week Host)
*Naboo (Old Dominion) - two possible projects, not certain yet - (1) smaller training datasets for medical images, (2) braining atlas segmentation integration with Slicer.
*Curt Lisle (KnowledgeVis) - (1) web-based transition of subset of Slicer capability, (2) Iowa Meshing Module move from 3.6 to 4.7 (with Alex from Kitware); (3) possible integration of web-based volume rendering;
*Patmal (Univ. of Pennsylvania Biomedical Group) Showcase some of their capability: cancer & phenomics toolkit; facilitate integration from their toolkit to/from Slicer; They have a few applications that might be interesting to others during project week - (1) fibre tracking in brain, (2) lung tumor segmentation, (3) density estimation
*Eric Seigler - OHIS Cornerstone Lesion Tracker author; Integrating interactive segmentation into the web-based framework

Discussions
*Training deep learning networks with smaller datasets.
*Integrating of projects with Slicer & architecture will be a discussion topic during the week.
*Everybody, be sure to register on the Wiki for the workshop; come up with a title and short description of the project(s) you will work on.
*OK to send info to the NA-MIC email list

===Tcon #2: November 22 ===
*10am Boston time
*Topics: continue Deep Learning, Web-based software
*Participants (interests)
# Tina Kapur, BWH (needle detection)
# Steve Pieper, Isomics (web-based software, GPU computing)
#Mahbub, Old Dominion University (creating architectures for deep learning networks in matlab, bridge to Slicer, MICCAI challenge code into Slicer, make learning faster and with less training)
#Siddiqi, Old Dominion
#Nicole Aucoin, BWH
#Abdul, NVIDIA Business Development (potential deep learning tutorials by web conference before the event, then at the event)
#Able Brown, NVIDIA
#JC, Kitware
#Krisztina Fischer, BWH
#Andrew Beard, MGH (deep learning, parameter estimation from MRI)
#Gordon Harris, MGH
#Laurent Chauvin, ETS Montreal (CNN, GPUs)
#John Toeber, Bremen U
#Lauren O'Donnell, BWH
#Fan Zhang, BWH
#Isaiah Norton, BWH
#Wu Ye, BWH
#Erik Siegler, MGH (Slicer / ePad connection with LesionTracker; more web-based technologies; discussion of ePad custom widget that will send out connections to remote jobs., PET/CT Lymphoma workshop as extension to lesion tracker.)
#Pete Anderson (Open source tracker)
#Guillaume Pernelle, Imperial College (CNNs, LSTMs, Deep Learning, GPUs)
#Mike Halle, BWH (slicer cross-platform architecture to work for hardware-specific; proxy running cloud that can support interface, Atlas browser is an active project for web-based interface. Slicer success is primarily application-level tools. The community could benefit with architectural study.)
#Louise, Oslo, Norway (high-speed pipelining of image processing for liver resections. )
#Paolo, Catanzaro, Italy (Multiple GPU system architecture)
#Salvatore, Catanzaro, Italy
#Daniel Rubin, Stanford
#Curt Lisle, Knowledgevis (has image web-based workflow systems; Deeply interested in web transition of imaging technologies. Also interested (but not experienced) in deep learning application; particularly feature recognition; small animal imaging applications)
#Charles Guttman, BWH (has MS data, wants to use Docker-based repository. (Boutique looks like a good repository)

===Tcon #1: November 15 ===
*10am Boston time
*Emergent interest in the application of Deep Learning techniques to medical image analysis.

Participants and interests
*#Tina Kapur, BWH -- needle detection
*#Steve Pieper, Isomics - GPUs
*#Andras Lasso, Queens - Slicer expert
*#Frankie Preiswerk, BWH -- hybrid ultrasound and MRI, interested in NVIDIA workshop
*#JC, Kitware -- Slicer expert
*#Mike Halle, BWH -- image feature search using deep learning (w/ Sandy Wells)
*#Peter Neher, DKFZ (filling in for Marco Nolden) -- experience with deep learning with face recognition
*#Nicole Aucoin, BWH -- Slicer expert
*#Alireza Mehrtash, UBC, BWH -- deep learning for prostate interventions, extension server for deep learning for Slicer
*#Daniel Rubin deep learning
*#Krisztina Fischer -- teaching

==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, January 9
!style="width:18%" |Tuesday, January 10
!style="width:18%" |Wednesday, January 11
!style="width:18%" |Thursday, January 12
!style="width:18%" |Friday, January 13
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"| '''Work on Projects'''
|bgcolor="#6494ec"| '''Work on Projects'''
|bgcolor="#6494ec"| '''Work on Projects'''
|bgcolor="#faedb6"| '''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|rowspan="2"|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9:00am-10:30am'''
|bgcolor="#88aaae"|Prostate Clinical Discussion with Clare Tempany (to be confirmed)
|bgcolor="#88aaae"|9:00am-10:00am: Shape Analysis Breakout ---- ---- ---- ---- ---- ---- 10:00am-12:00pm: Luis Ibanez TensorFlow Workshop (to be confirmed)
|bgcolor="#88aaae"|Prostate Cancer: Quantitative Imaging Network Discussion with Fiona Fennessy
|bgcolor="#88aaae"|Clinical Topics
|-
|bgcolor="#ffffdd"|'''10:30am-12pm:'''
|'''Talk (Sebastian Ourselin, PhD, UCL)'''
|
|
|
|Project Reporting
|-
|bgcolor="#ffffdd"|'''12:00pm-1:00pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1:00-5:30pm'''
|'''1:00pm-2:30pm:''' Project Presentations
|
|
|
|'''1:30-2:30pm:''' [[AMIGO_TourPW2017Winter|AMIGO Tour]]
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day [[2017_Winter_Project_Week/Dinner|Dinner on Thursday Night]] (Optional)
|
|}

==Calendar==

'''''The events are listed in the calendar below. Note that due to a current known limitation of our infrastructure, you will need to manually navigate to the week of January 8, 2017 to see the relevant events.''''' 

{{#widget:Google Calendar
|id=kitware.com_sb07i171olac9aavh46ir495c4@group.calendar.google.com
|timezone=America/New_York&dates=20170108%2F20170114
|title=NAMIC Winter Project Week
|view=WEEK
|dates=20170108/20170114
}}

iCal (.ics) link: https://calendar.google.com/calendar/ical/kitware.com_sb07i171olac9aavh46ir495c4%40group.calendar.google.com/public/basic.ics

='''Projects'''=

*Use this [[2017_Project_Week_Template | Updated Template for project pages]]

== Learning and GPUs ==

== Web Technologies ==

== Visualization ==
* [[2017 Winter Project Week/Slicer_HoloLens | Slicer & HoloLens]] (Adam Rankin)

== IGT ==
* [[2017 Winter Project Week/Tracked Ultrasound Standardization | Tracked Ultrasound Standardization III: The Refining]] (Andras Lasso, Simon Drouin, Junichi Tokuda, Longquan Chen, Adam Rankin, Janne Beate Bakeng)
* [[2017 Winter Project Week/WhiteMatterAnalysis | WhiteMatterAnalysis New Module and Documentation]] (Fan Zhang, Isaiah Norton, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/ROS Surface Scan | ROS Surface Scan]] (Tobias Frank, Junichi Tokuda, Longquan Chen)

== Craniofacial ==

== Infrastructure ==
* [[2017 Winter Project Week/Slicer Qt5 and Python3 | Slicer Qt5 and Python3]] (Steve Pieper, Jean-Christophe Fillion-Robin, Andras Lasso, Andrey Fedorov)
* [[2017 Winter Project Week/SlicerDMRIDocumentationAndTesting | SlicerDMRI Testing and Documentation]] (Isaiah Norton, Fan Zhang, Shun Gong, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/SubjectHierarchyRefactoring | Subject hierarchy refactoring]] (Csaba Pinter)

== DICOM ==

==To be Categorized==

* [[2017 Winter Project Week/HyperspectralOpht | Slicer for Hyperspectral Ophthalmology Analysis ]] (Sungmin Hong)

= '''Registrants''' =

Do not add your name to this list - it is maintained by the organizers based on your paid registration. To register, visit this [https://www.regonline.com/2017projectweek registration site].

2017 Winter Project Week/HyperspectralOpht

2016-12-13T20:21:16Z

Shong: Created page with "__NOTOC__ <gallery> Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|Projects List <!-- Use the "Upload file" link on the l..."

2017 Winter Project Week

2016-12-13T20:19:14Z

Shong: /* To be Categorized */

__NOTOC__
[[image:PW-Winter2017.png|300px]]

=Welcome to the web page for the 24th Project Week!=

The 24th NA-MIC Project Week open source hackathon is being held during the week of January 9-13, 2017 at MIT. Please go through this page for information, and if you have questions, please contact [https://www.spl.harvard.edu/pages/People/tkapur Tina Kapur, PhD].

==Logistics==

*'''Dates:''' January 9-13, 2017.
*'''Location:''' [https://www.google.com/maps/place/MIT:+Computer+Science+and+Artificial+Intelligence+Laboratory/@42.361864,-71.090563,16z/data=!4m2!3m1!1s0x0:0x303ada1e9664dfed?hl=en MIT CSAIL], Cambridge, MA. (Rooms: [[MIT_Project_Week_Rooms#Kiva|Kiva]], R&D)
*'''REGISTRATION:''' Register [https://www.regonline.com/2017projectweek here]. Registration Fee: $330.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.

== Introduction ==
The National Alliance for Medical Image Computing (NAMIC), was founded in 2005 and chartered with building a computational infrastructure to support biomedical research as part of the NIH funded [http://www.ncbcs.org/ NCBC] program. The work of this alliance has resulted in important progress in algorithmic research, an open source medical image computing platform [http://www.slicer.org 3D Slicer], enhancements to the underlying building blocks [http://www.vtk.org VTK], [http://www.itk.org ITK], [http://www.cmake.org CMake], and [http://www.cdash.org CDash], and the creation of a community of algorithm researchers, biomedical scientists and software engineers who are committed to open science. This community meets twice a year in an open source hackathon event called Project Week.

[[Engineering:Programming_Events|Project Week]] is a semi-annual open source hackathon which draws 60-120 researchers. As of August 2014, it is a [http://www.miccai.org/organization MICCAI] endorsed event. The participants work collaboratively on open-science solutions for problems that lie on the interfaces of the fields of computer science, mechanical engineering, biomedical engineering, and medicine. In contrast to conventional conferences and workshops the primary focus of the Project Weeks is to make progress in projects (as opposed to reporting about progress). The objective of the Project Weeks is to provide a venue for this community of medical open source software creators. Project Weeks are open to all, are publicly advertised, and are funded through fees paid by the attendees. Participants are encouraged to stay for the entire event.

Project Week activities: Everyone shows up with a project. Some people are working on the platform. Some people are developing algorithms. Some people are applying the tools to their research problems. We begin the week by introducing projects and connecting teams. We end the week by reporting progress. In addition to the ongoing working sessions, breakout sessions are organized ad-hoc on a variety of special topics. These topics include: discussions of software architecture, presentations of new features and approaches and topics such as Image-Guided Therapy.

Several funded projects use the Project Week as a place to convene and collaborate. These include [http://nac.spl.harvard.edu/ NAC], [http://www.ncigt.org/ NCIGT], [http://qiicr.org/ QIICR], and [http://ocairo.technainstitute.com/open-source-software-platforms-and-databases-for-the-adaptive-process/ OCAIRO].

A summary of all previous Project Events is available [[Project_Events#Past_Project_Weeks|here]].

This project week is an event [[Post-NCBC-2014|endorsed]] by the MICCAI society.

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

==Conference Calls for Preparation==

Domestic conference call number: 800-501-8979 
Access code: 7327389 

[[media:TollFree.png|International conference call numbers]] 
Access code: 7327389 

Weekly, Tuesdays 10am, Boston time from November 22nd, 2016 to January 3rd, 2017.

===Tcon #4: December 6 ===
*10am Boston time
*Project Discussions with focus on Machine Learning and Web-based software for clinical research/trials

Participants
* Tina Kapur (MIT) - organizer
* Steve Pieper (Isomics/BWH)
* Beatriz, JC (Kitware)

===Tcon #3: November 29 ===
*10am Boston time
*Project Discussions with focus on Machine Learning and Web-based software for clinical research/trials
Participants
* Tina Kapur (MIT) - organizer; planning Nvidia webinars. Proposal to use Monday as a schedule for the deep learning workshop before workshop begins.
*Steve Pieper (Isomics/BWH) - NVidia update; met with Abdul at NVidia (Medical applications of deep learning). Positive meeting and Slicer might be a reference platform. NVIdia will host a Monday Afternoon session at Project Week and hold outside webinars for basic deep learning information. Considering two or three days of a few hours each for webinar. Discussion of a prize board awarded for demonstrations that Nvidia could use.
*Patmal (Univ. of Pennsylvania Biomedical Group) Showcase some of their capability: cancer & phenomics toolkit; facilitate integration from their toolkit to/from Slicer; They have a few applications that might be interesting to others during project week - (1) fibre tracking in brain, (2) lung tumor segmentation, (3) density estimation
*Beatriz, Alexis, and JC (Kitware) - Slicer support and 3 other projects (1) customized childrern's medical, (1) Alex submitted volumetric meshing project in Slicer idea to the NA-MIC email list; have a discussion on Meshing; the goal is for Finite Element analsis and light imaging (2), better MATLAB integration (use Python-like interface). (3) Beatrix - shape analysis algorithms to be added to Slicer based on recently-funded grant.
*JC (Kitware) - integration of iPython and Slicer; Tina Suggested also Deep Infer framework to integrate with Slicer
*Louise Oram (Stockholm) - Specialization of Slicer for Liver surgery, possibly using SliceLet approach or other way to tailor the Slicer UI. Currently customizations need to be in the Extension catalog. Also interested in Liver vessel segmentation project and other Liver segmentation tasks. This might complement with MeVIS lab capability focused on liver. Separate modules for liver surgery that might be integrated together later.
*Frank Pricework (BWH) - General GPU programming; experiment with rehosting algorithms has has previously developed to GPU.
*Paolo, Italy - (Summer Project Week Host)
*Naboo (Old Dominion) - two possible projects, not certain yet - (1) smaller training datasets for medical images, (2) braining atlas segmentation integration with Slicer.
*Curt Lisle (KnowledgeVis) - (1) web-based transition of subset of Slicer capability, (2) Iowa Meshing Module move from 3.6 to 4.7 (with Alex from Kitware); (3) possible integration of web-based volume rendering;
*Patmal (Univ. of Pennsylvania Biomedical Group) Showcase some of their capability: cancer & phenomics toolkit; facilitate integration from their toolkit to/from Slicer; They have a few applications that might be interesting to others during project week - (1) fibre tracking in brain, (2) lung tumor segmentation, (3) density estimation
*Eric Seigler - OHIS Cornerstone Lesion Tracker author; Integrating interactive segmentation into the web-based framework

Discussions
*Training deep learning networks with smaller datasets.
*Integrating of projects with Slicer & architecture will be a discussion topic during the week.
*Everybody, be sure to register on the Wiki for the workshop; come up with a title and short description of the project(s) you will work on.
*OK to send info to the NA-MIC email list

===Tcon #2: November 22 ===
*10am Boston time
*Topics: continue Deep Learning, Web-based software
*Participants (interests)
# Tina Kapur, BWH (needle detection)
# Steve Pieper, Isomics (web-based software, GPU computing)
#Mahbub, Old Dominion University (creating architectures for deep learning networks in matlab, bridge to Slicer, MICCAI challenge code into Slicer, make learning faster and with less training)
#Siddiqi, Old Dominion
#Nicole Aucoin, BWH
#Abdul, NVIDIA Business Development (potential deep learning tutorials by web conference before the event, then at the event)
#Able Brown, NVIDIA
#JC, Kitware
#Krisztina Fischer, BWH
#Andrew Beard, MGH (deep learning, parameter estimation from MRI)
#Gordon Harris, MGH
#Laurent Chauvin, ETS Montreal (CNN, GPUs)
#John Toeber, Bremen U
#Lauren O'Donnell, BWH
#Fan Zhang, BWH
#Isaiah Norton, BWH
#Wu Ye, BWH
#Erik Siegler, MGH (Slicer / ePad connection with LesionTracker; more web-based technologies; discussion of ePad custom widget that will send out connections to remote jobs., PET/CT Lymphoma workshop as extension to lesion tracker.)
#Pete Anderson (Open source tracker)
#Guillaume Pernelle, Imperial College (CNNs, LSTMs, Deep Learning, GPUs)
#Mike Halle, BWH (slicer cross-platform architecture to work for hardware-specific; proxy running cloud that can support interface, Atlas browser is an active project for web-based interface. Slicer success is primarily application-level tools. The community could benefit with architectural study.)
#Louise, Oslo, Norway (high-speed pipelining of image processing for liver resections. )
#Paolo, Catanzaro, Italy (Multiple GPU system architecture)
#Salvatore, Catanzaro, Italy
#Daniel Rubin, Stanford
#Curt Lisle, Knowledgevis (has image web-based workflow systems; Deeply interested in web transition of imaging technologies. Also interested (but not experienced) in deep learning application; particularly feature recognition; small animal imaging applications)
#Charles Guttman, BWH (has MS data, wants to use Docker-based repository. (Boutique looks like a good repository)

===Tcon #1: November 15 ===
*10am Boston time
*Emergent interest in the application of Deep Learning techniques to medical image analysis.

Participants and interests
*#Tina Kapur, BWH -- needle detection
*#Steve Pieper, Isomics - GPUs
*#Andras Lasso, Queens - Slicer expert
*#Frankie Preiswerk, BWH -- hybrid ultrasound and MRI, interested in NVIDIA workshop
*#JC, Kitware -- Slicer expert
*#Mike Halle, BWH -- image feature search using deep learning (w/ Sandy Wells)
*#Peter Neher, DKFZ (filling in for Marco Nolden) -- experience with deep learning with face recognition
*#Nicole Aucoin, BWH -- Slicer expert
*#Alireza Mehrtash, UBC, BWH -- deep learning for prostate interventions, extension server for deep learning for Slicer
*#Daniel Rubin deep learning
*#Krisztina Fischer -- teaching

==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, January 9
!style="width:18%" |Tuesday, January 10
!style="width:18%" |Wednesday, January 11
!style="width:18%" |Thursday, January 12
!style="width:18%" |Friday, January 13
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"| '''Work on Projects'''
|bgcolor="#6494ec"| '''Work on Projects'''
|bgcolor="#6494ec"| '''Work on Projects'''
|bgcolor="#faedb6"| '''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|rowspan="2"|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9:00am-10:30am'''
|bgcolor="#88aaae"|Prostate Clinical Discussion with Clare Tempany (to be confirmed)
|bgcolor="#88aaae"|9:00am-10:00am: Shape Analysis Breakout ---- ---- ---- ---- ---- ---- 10:00am-12:00pm: Luis Ibanez TensorFlow Workshop (to be confirmed)
|bgcolor="#88aaae"|Prostate Cancer: Quantitative Imaging Network Discussion with Fiona Fennessy
|bgcolor="#88aaae"|Clinical Topics
|-
|bgcolor="#ffffdd"|'''10:30am-12pm:'''
|'''Talk (Sebastian Ourselin, PhD, UCL)'''
|
|
|
|Project Reporting
|-
|bgcolor="#ffffdd"|'''12:00pm-1:00pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1:00-5:30pm'''
|'''1:00pm-2:30pm:''' Project Presentations
|
|
|
|'''1:30-2:30pm:''' [[AMIGO_TourPW2017Winter|AMIGO Tour]]
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day [[2017_Winter_Project_Week/Dinner|Dinner on Thursday Night]] (Optional)
|
|}

==Calendar==

'''''The events are listed in the calendar below. Note that due to a current known limitation of our infrastructure, you will need to manually navigate to the week of January 8, 2017 to see the relevant events.''''' 

{{#widget:Google Calendar
|id=kitware.com_sb07i171olac9aavh46ir495c4@group.calendar.google.com
|timezone=America/New_York&dates=20170108%2F20170114
|title=NAMIC Winter Project Week
|view=WEEK
|dates=20170108/20170114
}}

iCal (.ics) link: https://calendar.google.com/calendar/ical/kitware.com_sb07i171olac9aavh46ir495c4%40group.calendar.google.com/public/basic.ics

='''Projects'''=

*Use this [[2017_Project_Week_Template | Updated Template for project pages]]

== Learning and GPUs ==

== Web Technologies ==

== Visualization ==
* [[2017 Winter Project Week/Slicer_HoloLens | Slicer & HoloLens]] (Adam Rankin)

== IGT ==
* [[2017 Winter Project Week/Tracked Ultrasound Standardization | Tracked Ultrasound Standardization III: The Refining]] (Andras Lasso, Simon Drouin, Junichi Tokuda, Longquan Chen, Adam Rankin, Janne Beate Bakeng)
* [[2017 Winter Project Week/WhiteMatterAnalysis | WhiteMatterAnalysis New Module and Documentation]] (Fan Zhang, Isaiah Norton, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/ROS Surface Scan | ROS Surface Scan]] (Tobias Frank, Junichi Tokuda, Longquan Chen)

== Craniofacial ==

== Infrastructure ==
* [[2017 Winter Project Week/Slicer Qt5 and Python3 | Slicer Qt5 and Python3]] (Steve Pieper, Jean-Christophe Fillion-Robin, Andras Lasso, Andrey Fedorov)
* [[2017 Winter Project Week/SlicerDMRIDocumentationAndTesting | SlicerDMRI Testing and Documentation]] (Isaiah Norton, Fan Zhang, Shun Gong, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/SubjectHierarchyRefactoring | Subject hierarchy refactoring]] (Csaba Pinter)

== DICOM ==

==To be Categorized==

* [[2017 Winter Project Week/HyperspectralOpht | 3D/4D Hyperspectral Ophthalmology Image Analysis ]] (Sungmin Hong)

= '''Registrants''' =

Do not add your name to this list - it is maintained by the organizers based on your paid registration. To register, visit this [https://www.regonline.com/2017projectweek registration site].