Difference between revisions of "2015 Summer Project Week:LungCAD"

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==Key Investigators==
 
==Key Investigators==
* Jayender Jagadeesan
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* Jayender Jagadeesan (BWH)
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* Tobias Penskofer (Charite, Berlin)
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* Sandy Wells (BWH)
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* Clara Meiner (previously BWH)
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* Raul San Jose Estepar (BWH)
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* Jorge Onieva (BWH)
  
 
==Project Description==
 
==Project Description==
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<h3>Objective</h3>
 
<h3>Objective</h3>
 
* Develop a module in 3D Slicer to segment the ground glass opacity (GGO) tumor, apply  [https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/HeterogeneityCAD HeterogeneityCAD]  to obtain imaging metrics and classify the GGO.
 
* Develop a module in 3D Slicer to segment the ground glass opacity (GGO) tumor, apply  [https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/HeterogeneityCAD HeterogeneityCAD]  to obtain imaging metrics and classify the GGO.
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* Provide the module as an extension part of [https://www.slicer.org/wiki/Documentation/Nightly/Extensions/OpenCAD OpenCAD]
 
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<h3>Progress</h3>
 
<h3>Progress</h3>
 
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* Completed analysis for 248 GGOs and trained SVM with an accuracy of 89% to classify GGOs
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* Developed the framework for the LungCAD module in Slicer
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* Evaluated the Lesion segmentation algorithm as part of the Chest Imaging Platform
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* Segmentation works well and is able to prevent the segmentation of vessels running through the lesion
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* LungCAD calls Lesion Segmentation CLI for segmentation and HeterogeneityCAD to evaluate features
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* Pre-processed SVM will be utilized to classify the GGOs
 
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Latest revision as of 18:07, 10 July 2017

Home < 2015 Summer Project Week:LungCAD

Key Investigators

  • Jayender Jagadeesan (BWH)
  • Tobias Penskofer (Charite, Berlin)
  • Sandy Wells (BWH)
  • Clara Meiner (previously BWH)
  • Raul San Jose Estepar (BWH)
  • Jorge Onieva (BWH)

Project Description

Objective

  • Develop a module in 3D Slicer to segment the ground glass opacity (GGO) tumor, apply HeterogeneityCAD to obtain imaging metrics and classify the GGO.
  • Provide the module as an extension part of OpenCAD

Approach, Plan

  • Implement a simple region growing algorithm
  • Apply HeterogeneityCAD module
  • Use predetermined SVM classifier to decide the lesion type

Progress

  • Completed analysis for 248 GGOs and trained SVM with an accuracy of 89% to classify GGOs
  • Developed the framework for the LungCAD module in Slicer
  • Evaluated the Lesion segmentation algorithm as part of the Chest Imaging Platform
  • Segmentation works well and is able to prevent the segmentation of vessels running through the lesion
  • LungCAD calls Lesion Segmentation CLI for segmentation and HeterogeneityCAD to evaluate features
  • Pre-processed SVM will be utilized to classify the GGOs