EM Segment

Key Investigators

• Sylvain Jaume, MIT
• Nicolas Rannou, BWH
• Koen Van Leemput, MGH
• Polina Golland, MIT
• Steve Pieper, BWH
• Ron Kikinis, BWH

Objective

The goal of this project is to create a segmentation module in Slicer3 that offers a high productivity and reliability to the clinician. The primary application is the segmentation of MRI images using a probabilistic atlas.

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

A Slicer3 module for MRI Bias Field Correction has been developed to serve as a pre-processing step for the EM segmentation and is currently tested by collaborators who study 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.