A Patient-Specific Segmentation Framework for Longitudinal MR Images of Traumatic Brain Injury.
Traumatic brain injury (TBI) is a major cause of death and disability worldwide. Robust, reproducible segmentations of MR images with TBI are crucial for quantitative analysis of recovery and treatment efficacy. However, this is a significant challenge due to severe anatomy changes caused by edema (swelling), bleeding, tissue deformation, skull fracture, and other effects related to head injury. In this paper, we introduce a multi-modal image segmentation framework for longitudinal TBI images. The framework is initialized through manual input of primary lesion sites at each time point, which are then refined by a joint approach composed of Bayesian segmentation and construction of a personalized atlas. The personalized atlas construction estimates the average of the posteriors of the Bayesian segmentation at each time point and warps the average back to each time point to provide the updated priors for Bayesian segmentation. The difference between our approach and segmenting longitudinal images independently is that we use the information from all time points to improve the segmentations. Given a manual initialization, our framework automatically segments healthy structures (white matter, grey matter, cerebrospinal fluid) as well as different lesions such as hemorrhagic lesions and edema. Our framework can handle different sets of modalities at each time point, which provides flexibility in analyzing clinical scans. We show results on three subjects with acute baseline scans and chronic follow-up scans. The results demonstrate that joint analysis of all the points yields improved segmentation compared to independent analysis of the two time points.
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah ; School of Computing, University of Utah, Salt Lake City, Utah.
Bo
B
Wang
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah ; School of Computing, University of Utah, Salt Lake City, Utah.
Marcel
M
Prastawa
Laboratory of Neuro Imaging, University of California, Los Angeles, California.
Andrei
A
Irimia
Laboratory of Neuro Imaging, University of California, Los Angeles, California ; Henri Samueli School of Engineering and Applied Science, University of California, Los Angeles, California.
Micah C
MC
Chambers
Brain Injury Research Center, Departments of Neurosurgery and Neurology, University of California, Los Angeles, California.
Paul M
PM
Vespa
Laboratory of Neuro Imaging, University of California, Los Angeles, California.
John D
JD
Van Horn
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah ; School of Computing, University of Utah, Salt Lake City, Utah.
Guido
G
Gerig
Atlas formation; Image segmentation; Longitudinal analysis