Difference between revisions of "2013 Project Week:QualityAssuranceModule"

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Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
 
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
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Image:ScarSeg_EM.png‎| Scar tissue identification.
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Image:QAModule_derived.png‎| Derived Images QA module
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Image:QAModule_rawDWI.png‎| Raw DWI QA module
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Image:QAModule_postDWI.png‎| Processed DWI QA module
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Image:QAModule_db.png‎| PostgresSQL database
 
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==Key Investigators==
 
==Key Investigators==
  
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* LiangJia Zhu, Allen Tannenbaum, UAB
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* Dave Welch, UIowa SENAP
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* Yi Gao, BWH
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* Hans Johnson, UIowa SENAP
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* Josh Cates, Rob MacLeod, SCI
 
  
 
==Project Description==
 
==Project Description==
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<h3>Objective</h3>
 
<h3>Objective</h3>
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* We are developing methods for identifying scar tissue from CARMA data. Our previous method demonstrates an effective identification ability for DE-MRI data. In this method, the intensity distribution inside the LA myocardial wall is modeled as a mixture of Gaussians. To improve the performance of this method, we will integrate the intensity information from the LA chamber into the overall identification procedure.
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* We have created several Python-based image quality assurance modules for use by research technicians in our lab. Our goals with this project are:
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* We will discuss possible improvements for scar identification.
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# Refactoring to enable more flexible extension of the modules
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# Implementation of several user requests, such as hotkeys
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# Improved interaction with our database for increased robustness
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# Expanded testing and logging
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# Documentation and examples on Slicer Wiki
 
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
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* Design an identification scheme using the LA intensity as a prior
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* Implement a test database on the fly
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* Test the method using CARMA data
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* Expand test code coverage (doctests, unittests via nose)
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* Deliver the implementation in CLI module.
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* Add logging
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* Decouple module code from DB interaction
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* Refactor and consolidate code from our three modules
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* Improve GUI and MRML interactions
 
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Revision as of 15:38, 7 January 2013

Home < 2013 Project Week:QualityAssuranceModule

  • Derived Images QA module

  • Raw DWI QA module

  • Processed DWI QA module

  • PostgresSQL database

Key Investigators

  • Dave Welch, UIowa SENAP
  • Hans Johnson, UIowa SENAP

Project Description

Objective

  • We have created several Python-based image quality assurance modules for use by research technicians in our lab. Our goals with this project are:
  1. Refactoring to enable more flexible extension of the modules
  2. Implementation of several user requests, such as hotkeys
  3. Improved interaction with our database for increased robustness
  4. Expanded testing and logging
  5. Documentation and examples on Slicer Wiki

Approach, Plan

  • Implement a test database on the fly
  • Expand test code coverage (doctests, unittests via nose)
  • Add logging
  • Decouple module code from DB interaction
  • Refactor and consolidate code from our three modules
  • Improve GUI and MRML interactions

Progress

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