Traumatic brain injury (TBI) can cause severe disorders, including post-traumatic epilepsy (PTE). Lesion segmentation is an MRI-based analysis to identify brain structures that correlate with PTE development post-TBI. Unfortunately, manual segmentation, considered the gold standard, is highly tedious and noisy. Thus, we propose the first automated machine-learning based lesion segmentation method for MRI of TBI patients enrolled in the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx). Experimental validation demonstrates considerable visual overlap of lesion predictions and ground-truths with 61% precision. Early and automated lesion segmentation via our approach can aid experts in MRI analysis and successful PTE identification following TBI.
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