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.