Abstract Brain MRI segmentation plays a crucial role in medical imaging, aiding in the identification and monitoring of brain diseases. This research presents a novel deep learning-based framework designed to achieve high segmentation accuracy while maintaining a lightweight architecture suitable for real-world deployment. The proposed method utilizes EfficientNet B0 as an encoder, which ensures rich multi-scale feature extraction with significantly reduced model complexity. To enhance global context modeling without increasing the computational burden, the framework incorporates Visual State-Space blocks. These blocks leverage patch merging and state-space modeling to capture long-range spatial dependencies efficiently. Additionally, a multi-scale attention mechanism inspired by the Mamba architecture is introduced to refine feature representations across different scales, improving the network’s ability to segment complex anatomical structures and lesions. The decoder follows a U-Net-inspired design, integrating skip connections to preserve spatial details and enable high-resolution segmentation map reconstruction. The training process is optimized using a hybrid loss function, combining Active Contour Loss for precise boundary delineation and Focal Loss mitigates class imbalance, ensuring robust segmentation performance. By effectively balancing segmentation accuracy with a lightweight model design, the proposed approach provides visually superior segmentation results compared to other state-of-the-art.
Chatterjee et al. (Tue,) studied this question.