Difference between revisions of "EMSegment"

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==Key Investigators==
 
==Key Investigators==
* Sylvain Jaume, Polina Golland (MIT Computer Science and Artificial Intelligence Laboratory)
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* Sylvain Jaume, Koen Van Leemput, Polina Golland (MIT Computer Science and Artificial Intelligence Laboratory)
 
* Nicolas Rannou, Steve Pieper, Ron Kikinis (Harvard Medical School, Brigham and Women's Hospital)
 
* Nicolas Rannou, Steve Pieper, Ron Kikinis (Harvard Medical School, Brigham and Women's Hospital)
* Koen Van Leemput (Harvard Medical School, Massachusetts General Hospital)
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==Collaboration==
 
==Collaboration==
 
* [http://www.na-mic.org/Wiki/index.php/Projects:BayesianMRSegmentation Bayesian Segmentation of MRI Images] by Koen Van Leemput, Sylvain Jaume, Polina Golland, Steve Pieper and Ron Kikinis.
 
* [http://www.na-mic.org/Wiki/index.php/Projects:BayesianMRSegmentation Bayesian Segmentation of MRI Images] by Koen Van Leemput, Sylvain Jaume, Polina Golland, Steve Pieper and Ron Kikinis.
 
* [http://www.na-mic.org/Wiki/index.php/Measuring_Alcohol_Stress_Interaction NA-MIC NCBC Collaboration: Measuring Alcohol and Stress Interaction] by Vidya Rajagopalan, Andriy Fedorov and Chris Wyatt.
 
* [http://www.na-mic.org/Wiki/index.php/Measuring_Alcohol_Stress_Interaction NA-MIC NCBC Collaboration: Measuring Alcohol and Stress Interaction] by Vidya Rajagopalan, Andriy Fedorov and Chris Wyatt.
 
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Revision as of 15:09, 17 June 2009

Home < EMSegment

Key Investigators

  • Sylvain Jaume, Koen Van Leemput, Polina Golland (MIT Computer Science and Artificial Intelligence Laboratory)
  • Nicolas Rannou, Steve Pieper, Ron Kikinis (Harvard Medical School, Brigham and Women's Hospital)


Objective

The goal of the EMSegment project is to create a segmentation module in Slicer3 that offers a high productivity and reliability to the clinician. The targetted application is the segmentation of MRI images of the brain. The EMSegment module allows the segmentation of other anatomical regions as long as a statistical atlas is available.

Approach, Plan

Our algorithm builds upon the Expectation Maximization theory and is structured to let the clinician make the most efficient use of his/her anatomical knowledge. Because of our intuitive visualization of probabilities, the user can understand the probabilistic features of his/her data and can efficiently tune the parameters to obtain the most accurate segmentation. To meet the time constraints of the tasks in a hospital, a main focus has been devoted to the acceleration of the EM segmentation algorithm.

Progress

The EMSegment module is under active development and will be available in Slicer 3.5. A first achievement of this project is the completion of a Slicer3 module for MRI Bias Field Correction. The MRIBiasFieldCorrection module is currently tested for the study of alcohol and stress interaction in primates.

Collaboration