Difference between revisions of "2010 Winter Project Week WMLS"
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Image:itk_wmls_pipeline.png| Pipeline of WML segmentation | Image:itk_wmls_pipeline.png| Pipeline of WML segmentation | ||
Image:UNCWMLSegmentation.png|One training dataset (T1, T2, PD, FLAIR images and wml segmentation) | Image:UNCWMLSegmentation.png|One training dataset (T1, T2, PD, FLAIR images and wml segmentation) | ||
| − | Image: | + | Image:itk_wmls.png| One testing dataset and segmentation result |
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<h3>Objective</h3> | <h3>Objective</h3> | ||
| − | We will continue developing and testing | + | We will continue developing and testing the white matter lesion segmentation algorithm implemented using ITK. The goal is to have an initial version ready by the end of the week that can be distributed within NA-MIC community for more extensive testing. |
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<h3>Approach, Plan</h3> | <h3>Approach, Plan</h3> | ||
| − | We will | + | We will develop a Slicer module for the white matter lesion segmentation algorithm. Base line results and test will be generated. |
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<h3>Progress</h3> | <h3>Progress</h3> | ||
| − | + | Since summer project week in Boston, we have developed/implemented a WML segmentation algorithm using ITK classes. Also, we have integrated into the Slicer3. We have made the tutorial for this project meeting to present how to use our learning-based white matter lesion segmenation algorithm in Slicer 3. Subtasks implemented include: (1) a skull stripping algorithm working on T1 weighted images; (2) a fuzzy clustering algorithm for tissue segmentation; (3) a parametric model for gain field correction. All of these subtasks are implemented by using ITK. The training step uses AdaBoost and the segmenation step uses a support vector machine. | |
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Latest revision as of 19:17, 16 June 2010
Home < 2010 Winter Project Week WMLS
Pipeline of WML segmentation
One training dataset (T1, T2, PD, FLAIR images and wml segmentation)
One testing dataset and segmentation result
Key Investigators
- UNC: Minjeong Kim, Dinggang Shen
- GE : Xiaodong Tao, Jim Miller
Objective
We will continue developing and testing the white matter lesion segmentation algorithm implemented using ITK. The goal is to have an initial version ready by the end of the week that can be distributed within NA-MIC community for more extensive testing.
Approach, Plan
We will develop a Slicer module for the white matter lesion segmentation algorithm. Base line results and test will be generated.
Progress
Since summer project week in Boston, we have developed/implemented a WML segmentation algorithm using ITK classes. Also, we have integrated into the Slicer3. We have made the tutorial for this project meeting to present how to use our learning-based white matter lesion segmenation algorithm in Slicer 3. Subtasks implemented include: (1) a skull stripping algorithm working on T1 weighted images; (2) a fuzzy clustering algorithm for tissue segmentation; (3) a parametric model for gain field correction. All of these subtasks are implemented by using ITK. The training step uses AdaBoost and the segmenation step uses a support vector machine.
References
- Zhiqiang Lao, Dinggang Shen, Dengfeng Liu, Abbas F Jawad, Elias R Melhem, Lenore J Launer, Nick R Bryan, Christos Davatzikos, Computer-Assisted Segmentation of White Matter Lesions in 3D MR images, Using Pattern Recognition, Academic Radiology, 15(3):300-313, March 2008.
