Difference between revisions of "2016 Winter Project Week/Projects/BatchImageAnalysis"

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</gallery>
 
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[[File:LungCT-3DSIFT.png|200px|thumb|left|3D SIFT Lung Features]]
  
 
==Key Investigators==
 
==Key Investigators==
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* Kalli Retzepi (MGH)
 
* Kalli Retzepi (MGH)
 
* Yangming Ou (MGH)
 
* Yangming Ou (MGH)
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* Matt Toews (?)
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* Matt Toews (ETS)
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* Lilla Zollei (MGH)
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* Lauren O'Donnell (BWH)
 
* Steve Pieper (BWH)
 
* Steve Pieper (BWH)
 
* Sandy Wells (BWH)
 
* Sandy Wells (BWH)
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==Project Description==
 
==Project Description==
 
{| class="wikitable"
 
{| class="wikitable"
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! style="text-align: left; width:27%" |  Objective
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! style="text-align: left; width:27%" |  Approach and Plan
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! style="text-align: left; width:27%" |  Progress and Next Steps
 
|- style="vertical-align:top;"
 
|- style="vertical-align:top;"
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! style="text-align: left; width:27%" |       Objective
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|
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<!-- Add a bulleted list of key points -->
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<!-- Objective bullet points -->
 
* Run feature detection code over a collection of medical images pulled from PACS
 
* Run feature detection code over a collection of medical images pulled from PACS
 
* Investigate a collection of ADC maps of neonates (diffusion MR)
 
* Investigate a collection of ADC maps of neonates (diffusion MR)
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* Use 3D SIFT code to see if health status can be detected in images
 
* Use 3D SIFT code to see if health status can be detected in images
 
* (if time) try text analysis of radiology reports
 
* (if time) try text analysis of radiology reports
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! style="text-align: left; width:27%" |       Approach, Plan
 
 
<!-- Add a bulleted list of key points -->
 
<!-- Add a bulleted list of key points -->
 
* Use deidentified cohort of neonate images collected from MGH
 
* Use deidentified cohort of neonate images collected from MGH
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* Explore visualization options  
 
* Explore visualization options  
 
* (if time) integrate image features with analysis of radiology report text
 
* (if time) integrate image features with analysis of radiology report text
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|
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<!-- Fill this out at the end of Project Week; describe what you did this week and what you plan to do next -->
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Algorithm
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* feature extraction (20 seconds per image)
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* Feature matching O(log N) indexing (< 1 second per image)
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* 3D SIFT-Rank code (Windows, Linux, Max)  and read me
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http://www.matthewtoews.com/fba/featExtract1.5.zip
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Result
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Baseline HIE classification rate: 73%, leave-one-out moderate vs normal.
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! style="text-align: left; width:27%" |      Progress
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Data
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<!-- Fill this out at the end of Project Week; describe what you did this week and what you plan to do next -->
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231 subjects, Apparent Diffusion Coefficient (ADC) images.
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*
 
  
 
|}
 
|}
  
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==Background and References==
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==Features Extracted in ADC MRI Volume ==
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<!-- 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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http://wiki.na-mic.org/Wiki/images/b/b2/Image_%282%29.png

Latest revision as of 15:54, 8 January 2016

Home < 2016 Winter Project Week < Projects < BatchImageAnalysis
3D SIFT Lung Features

Key Investigators

  • Kalli Retzepi (MGH)
  • Yangming Ou (MGH)
  • Matt Toews (ETS)
  • Lilla Zollei (MGH)
  • Lauren O'Donnell (BWH)
  • Steve Pieper (BWH)
  • Sandy Wells (BWH)
  • Randy Gollub (MGH)

Project Description

Objective Approach and Plan Progress and Next Steps
  • Run feature detection code over a collection of medical images pulled from PACS
  • Investigate a collection of ADC maps of neonates (diffusion MR)
  • Patients labeled with age and health status (normal, mildly abnormal, severely abnormal)
  • Use 3D SIFT code to see if health status can be detected in images
  • (if time) try text analysis of radiology reports
  • Use deidentified cohort of neonate images collected from MGH
  • Install data and software on AWS, try StarCluster
  • Explore visualization options
  • (if time) integrate image features with analysis of radiology report text

Algorithm

  • feature extraction (20 seconds per image)
  • Feature matching O(log N) indexing (< 1 second per image)
  • 3D SIFT-Rank code (Windows, Linux, Max) and read me

http://www.matthewtoews.com/fba/featExtract1.5.zip

Result Baseline HIE classification rate: 73%, leave-one-out moderate vs normal.


Data 231 subjects, Apparent Diffusion Coefficient (ADC) images.

Features Extracted in ADC MRI Volume

Image_%282%29.png

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