Moderate-to-Severe Traumatic Brain Injury (msTBI) often leads to complex and highly heterogeneous structural damage in the brain. Lesions may be focal or diffuse and can involve multiple tissue types, including gray matter, white matter, and cerebrospinal fluid. They also exhibit considerable variability in size, shape, spatial distribution, and hemispheric symmetry. This high degree of heterogeneity greatly increases the difficulty of automatic segmentation based on unimodal T1-weighted MRI. To address this challenge, we propose a customized optimization of the SegMamba architecture. The resulting optimized version, Brain Lesion SegMamba (BLSegMamba), retains the core structural components of SegMamba while integrating a more robust data augmentation strategy and a loss function specifically designed for the segmentation of msTBI lesions. On the final test dataset of the AIMS-TBI Challenge, our BLSegMamba achieves the top overall ranking after weighted aggregation of all evaluation metrics. Our code is publicly available at https://github.com/YueyueZhu/BLSegMamba .