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Proceedings Paper

A patient-specific segmentation framework for longitudinal MR images of traumatic brain injury
Author(s): Bo Wang; Marcel Prastawa; Andrei Irimia; Micah C. Chambers; Paul M. Vespa; John D. Van Horn; Guido Gerig
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Paper Abstract

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.

Paper Details

Date Published: 14 February 2012
PDF: 7 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831402 (14 February 2012); doi: 10.1117/12.911043
Show Author Affiliations
Bo Wang, The Univ. of Utah (United States)
Marcel Prastawa, The Univ. of Utah (United States)
Andrei Irimia, Univ. of California, Los Angeles (United States)
Micah C. Chambers, Univ. of California, Los Angeles (United States)
Paul M. Vespa, Univ. of California, Los Angeles (United States)
John D. Van Horn, Univ. of California, Los Angeles (United States)
Guido Gerig, The Univ. of Utah (United States)


Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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