EMSegment

Key Investigators

• Sylvain Jaume, MIT Computer Science and Artificial Intelligence Laboratory
• Nicolas Rannou, Harvard Medical School, Brigham and Women's Hospital
• Koen Van Leemput, Harvard Medical School, Massachusetts General Hospital
• Polina Golland, MIT Computer Science and Artificial Intelligence Laboratory
• Steve Pieper, Harvard Medical School, Brigham and Women's Hospital
• 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 MRI Bias Field Correction module. The MRIBiasFieldCorrection module is currently applied for the study of alcohol and stress interaction in primates.

Collaboration

• Bayesian Segmentation of MRI Images by Koen Van Leemput, Sylvain Jaume, Polina Golland, Steve Pieper and Ron Kikinis.
• NA-MIC NCBC Collaboration: Measuring Alcohol and Stress Interaction by Vidya Rajagopalan, Andriy Fedorov and Chris Wyatt.