Semiautomatic longitudinal segmentation of MR volumes in traumatic brain injury

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  Projects List

Key Investigators

• UCLA: Andrei Irimia, Micah Chambers, Jack Van Horn
• Kitware: Danielle Pace, Stephen Aylward
• University of Utah: Bo Wang, Marcel Prastawa, Guido Gerig

Objective

For the purposes of this project, we have been interacting frequently for the purpose of developing methods for analyzing MR volumes of traumatic brain injury. The goal is to provide tools for the automatic segmentation of TBI from multimodal neuroimaging volume data. The Utah team has been focusing on the development of semi-automatic segmentation of TBI volumes, and the UCLA team has made appreciable progress on patient data analysis and quantification of results.

Approach

The UCLA and Utah teams will be interacting and providing feedback with regard to an upcoming Slicer implementation of an automatic segmentation algorithm for TBI. This algorithm has been designed by Dr. Gerig's group and has been successfully applied to TBI data sets acquired at UCLA. During the Project Week we will focus on the analysis of additional subjects as well as on making plans to create a Slicer module to perform segmentation of TBI volumes via personalized atlas creation.

Progress

At UCLA, we have acquired additional data sets of MRI/DTI from TBI patients. These data were shared with our colleagues in Utah and, at the meeting in Boston, we plan on working to investigate the performance of the Utah algorithm on these data sets. We have also been sharing ideas regarding how best to incorporate these tools into Slicer, and will continue to do so at the Meeting.

Delivery Mechanism

We foresee that the Utah algorithm will be made available in Slicer upon further testing and evaluation. This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)

1. Slicer Module