Difference between revisions of "Project Week 25/Multimodal:"

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==Project Description==
 
==Project Description==
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* '''Project details can downloaded here as [http://research.engineering.nyu.edu/~fishbaugh/docs/Gerig-Slicer-6-26-2017-final.pptx pptx] or [https://na-mic.org/w/images/a/ae/Gerig-Slicer-6-26-2017-final.pdf pdf].'''
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* '''Download a [https://na-mic.org/w/images/a/af/Tutorial_HOA.pdf Slicer tutorial].'''
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{| class="wikitable"
 
{| class="wikitable"
 
! style="text-align: left; width:27%" |  Objective
 
! style="text-align: left; width:27%" |  Objective
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<!-- Objective bullet points -->
 
<!-- Objective bullet points -->
  
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3D/4D Ophthalmology Image Anaylsis Framework
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3D/4D Ophthalmology Image Anaylsis Framework: Continuation of initial January 2017 project by Sungmin Hong:
 
* Read 4D hyperspectral data
 
* Read 4D hyperspectral data
 
* Viewer and interactor for 3D hi-res image data and 4D hyperspectral data  
 
* Viewer and interactor for 3D hi-res image data and 4D hyperspectral data  
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** Implement/integrate 4D hyperspectral data viewer to show image and spectral information.
 
** 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 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
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** Explore most recent "Segment Editor" features for cell and organelle segmentation  
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** Implement a viewer and an interactor for cell statistics
 
  
 
* User Manual
 
* User Manual
 
** Create an user manual to comprehend a overview of an extension
 
** Create an user manual to comprehend a overview of an extension
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** 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.
 
  
 
|
 
|
 
<!-- Progress and Next steps bullet points (fill out at the end of project week -->
 
<!-- Progress and Next steps bullet points (fill out at the end of project week -->
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* Hyperspectral Analysis
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* Manual
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** Implemented a module to convert 4D LSM data to a series of 3D data compatible to MultiVolume Explorer
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** [https://na-mic.org/w/images/a/af/Tutorial_HOA.pdf Slicer tutorial] explaining multi-volume conversion and 3D/4D registration.
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** With a converted series of 3D data, MultiVolume explorer offered a basic analysis tool for hyperspectral data.
 
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** Label statistics or segmentation need to be added in the future
 
  
 
* Registration
 
* Registration
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** Basic registration algorithms in Slicer worked well on linear registration between 3D SIM and a cropped and dimension reduced 4D LSM data.  
+
** '''Basic registration algorithms in Slicer worked well for linear registration''' between 3D SIM and cropped and dimension reduced 4D LSM data.  
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** Detecting corresponding regions of 3D SIM in 4D LSM data needs to be developed in the future.
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** Automatic detection of corresponding subregions between hires 3D SIM and low-res 4D LSM data may be implemented via autocorrelation.
  
 
* Segmentation  
 
* Segmentation  
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** Watershed segmentation on 3D hi-res image data (WASP) was not successful.
+
** '''GrowCut segmentation worked well for 3D segmentation''' of organelles of different contrast, e.g. dark Lipofuscin and bright Melanolipofuscin as required by clinical researchers.
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** Editor/Segmentation Editor worked good on slice-by-slice segmentation
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** Cell segmentation/outlining and combination with organelle segmentation can be solved with the Segment Editor features.
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** Will investigate more on 3D segmentation capability of Slicer with possible collaboration with other groups.
+
 
 +
* Hyperspectral Analysis
 +
** Module to convert 4D LSM data to a series of 3D data compatible to MultiVolume Explorer was implemented at Jan. 2017 project week.
 +
** With such series of 3D data, MultiVolumeExplorer offers very basic qualitative analysis hyperspectral data by plotting a spectral curve per voxel.
 +
** '''Quantitative label statistics''' by combining 3D segmentation labels with 4D hyperspectral data '''so far solved via external python script''', will need to be added to the multivolume explorer in the future.
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|}
 
|}
  
 
==Illustrations==
 
==Illustrations==
  
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[[File:SIM-LSM-granules-for-Wiki.png]]
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[[File:SIM-LSM-granules-for-Wiki.png | 900px]]
  
 
==Background and References==
 
==Background and References==
 
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data -->
 
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data -->
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[1] Thomas Ach, Sungmin Hong, Rainer Heintzmann, Jost Hillenkamp, Kenneth R. Sloan, Neel Dey, Guido Gerig, R. Theodore Smith, Christine A. Curcio, Katharina Bermond, High-resolution and hyperspectral imaging of autofluorescent retinal pigment epithelium (RPE) granules, ARVO 2017 Annual Meeting Abstracts, No.  3382, [http://www.arvo.org/webs/am2017/sectionpdf/RC/Session%20363%20Macular%20degeneration-cell%20biology.pdf Abstract]
 +
 +
[2] Tong Y, Ben Ami T, Hong S, Heintzmann R, Gerig G, Ablonczy Z, Curcio CA, Ach T, Smith RT., HYPERSPECTRAL AUTOFLUORESCENCE IMAGING OF DRUSEN AND RETINAL PIGMENT EPITHELIUM IN DONOR EYES WITH AGE-RELATED MACULAR DEGENERATION, Retina. 2016 Dec;36 Suppl 1:S127-S136, [http://journals.lww.com/retinajournal/Citation/2016/12001/HYPERSPECTRAL_AUTOFLUORESCENCE_IMAGING_OF_DRUSEN.14.aspx Paper]

Latest revision as of 10:29, 30 June 2017

Home < Project Week 25 < Multimodal:

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Key Investigators

  • Guido Gerig (NYU Tandon School of Engineering, USA)
  • Sungmin Hong (NYU Tandon School of Engineering, USA)

Project Description


Objective Approach and Plan Progress and Next Steps

3D/4D Ophthalmology Image Anaylsis Framework: Continuation of initial January 2017 project by Sungmin Hong:

  • 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
    • Explore most recent "Segment Editor" features for cell and organelle segmentation
  • User Manual
    • Create an user manual to comprehend a overview of an extension
  • Manual
  • Registration
    • Basic registration algorithms in Slicer worked well for linear registration between 3D SIM and cropped and dimension reduced 4D LSM data.
    • Automatic detection of corresponding subregions between hires 3D SIM and low-res 4D LSM data may be implemented via autocorrelation.
  • Segmentation
    • GrowCut segmentation worked well for 3D segmentation of organelles of different contrast, e.g. dark Lipofuscin and bright Melanolipofuscin as required by clinical researchers.
    • Cell segmentation/outlining and combination with organelle segmentation can be solved with the Segment Editor features.
  • Hyperspectral Analysis
    • Module to convert 4D LSM data to a series of 3D data compatible to MultiVolume Explorer was implemented at Jan. 2017 project week.
    • With such series of 3D data, MultiVolumeExplorer offers very basic qualitative analysis hyperspectral data by plotting a spectral curve per voxel.
    • Quantitative label statistics by combining 3D segmentation labels with 4D hyperspectral data so far solved via external python script, will need to be added to the multivolume explorer in the future.

Illustrations

SIM-LSM-granules-for-Wiki.png

Background and References

[1] Thomas Ach, Sungmin Hong, Rainer Heintzmann, Jost Hillenkamp, Kenneth R. Sloan, Neel Dey, Guido Gerig, R. Theodore Smith, Christine A. Curcio, Katharina Bermond, High-resolution and hyperspectral imaging of autofluorescent retinal pigment epithelium (RPE) granules, ARVO 2017 Annual Meeting Abstracts, No. 3382, Abstract

[2] Tong Y, Ben Ami T, Hong S, Heintzmann R, Gerig G, Ablonczy Z, Curcio CA, Ach T, Smith RT., HYPERSPECTRAL AUTOFLUORESCENCE IMAGING OF DRUSEN AND RETINAL PIGMENT EPITHELIUM IN DONOR EYES WITH AGE-RELATED MACULAR DEGENERATION, Retina. 2016 Dec;36 Suppl 1:S127-S136, Paper

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