Difference between revisions of "Test Page"

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<strong>Real 3D object with multiple class</strong> – Here we use 2 volume conductor segmentation with 8 classes. The Dice score is calculated using a known software package nipipe (<a href="http://nipype.readthedocs.io" class="uri">http://nipype.readthedocs.io</a>). These objects are then passed on to our pipeline and compared against the manually calculated value.</li></ul></body></html>
 
<strong>Real 3D object with multiple class</strong> – Here we use 2 volume conductor segmentation with 8 classes. The Dice score is calculated using a known software package nipipe (<a href="http://nipype.readthedocs.io" class="uri">http://nipype.readthedocs.io</a>). These objects are then passed on to our pipeline and compared against the manually calculated value.</li></ul></body></html>
  
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<math>pi=frac{3}{4} sqrt{3}+24 int_0^{1/4}{sqrt{x-x^2}dx}</math>
  
 
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== References ==
 
== References ==
 
<references />
 
<references />

Revision as of 10:38, 28 April 2017

Contents

This is a test page

This is a reference for The Origins of EEG [1]


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2.52 6.71 10.07 5.04 8.39 5.04 10.07 4.20 6.71 11.75 5.04 12.59 5.04 9.23 5.04 10.07 4.20 10.07 8.39 7.55 8.39 5.87 11.75 3.36 5.04 1.68 7.55 2.52 10.91 5.04 6.71 5.04 2.52 5.04 12.59 11.75 14.27 5.87 6.71 6.71 7.55 2.52 13.43 15.11 5.87 8.39 3.36 7.55 8.39 13.43 2.52 8.39


<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8" /><meta http-equiv="Content-Style-Type" content="text/css" /><meta name="generator" content="pandoc" /><title></title><style type="text/css">code{white-space: pre;}</style></head><body>

Regression testing of BESA MRI Volume Conductor Segmentation

Regression testing for BM is done by comparing the volume conductor segmentation output between the old and the new version. Volume conductor segmentation is the last step in the segmentation pipeline of BESA MRI and any problems in the pipeline would be noticed when evaluating them. This comparison is done using Dice score. Dice Score is a standard metric used to measure the overlap between the 2 segmentation. The range of Dice score ranges from 0 to 1 indicating no overlap and complete overlap respectively. It is mathematically defined as $DSC = \frac{2\left| A \cap B \right|}{\left| A \right| + |B|}$, where A and B are 2 matrices of equal size and |A| and |B| are the number of elements present in the respectively.

Testing pipeline

The testing pipeline reads the volume conductor segmentation data from the old and the new version and then compares them using Dice score.

Data Required:
  • Project data from Reference (old) version.
  • Project data from the version to be tested.
  • List of Project name and path to be tested.

Pre-Processing

  • Change the MATLAB code (BatchCompareFiles_MMYYYY.m) to reflect the correct data folder for Reference and Testing (DataFolderRef,DataFolderTest).
  • Create a 2 text files that contains the project name and its respective project path.
  • Make sure the files containing the list of project name and project path (ProjectNamesFilename,ProjectPathsFilename) reflect the actual location.

Verification<img 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" width="239" height="192" />

After completing the 2 steps mentioned above the MATLAB script for regression test can be executed. After the test is over, a Result.txt is generated that will have the result of the test. It will have Dice scores between all the pair of images for all tissue classes. Ideally all the 8 values should be 1.0.

Validation of Test Script

To validate our test script, we verify the dice score of known 2D and 3D objects. Below is a list of all the cases that we have verified.
  • 2D object with single class – Here we generate a couple of 2D binary images (5x5). The Dice score is pre-calculated manually. These images then passed on to our pipeline and compared against the manually calculated value.
  • 3D object with single class – Here we generate a couple of 3D binary object (10x10x10). The Dice score is pre-calculated manually. These objects are then passed on to our pipeline and compared against the manually calculated value.
  • 3D object with multiple class – Here we generate a couple of 3D object (10x10x10) having 2 classes. The Dice score is pre-calculated manually. These objects are then passed on to our pipeline and compared against the manually calculated value.
  • Real 3D object with multiple class – Here we use 2 volume conductor segmentation with 8 classes. The Dice score is calculated using a known software package nipipe (<a href="http://nipype.readthedocs.io" class="uri">http://nipype.readthedocs.io</a>). These objects are then passed on to our pipeline and compared against the manually calculated value.
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[math]pi=frac{3}{4} sqrt{3}+24 int_0^{1/4}{sqrt{x-x^2}dx}[/math]

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References

  1. Millet, David (2002). "The Origins of EEG". International Society for the History of the Neurosciences (ISHN).