MEM-UNet: Morphology-Enhanced 3D Mamba UNet for Esophagus Segmentation.

Abstract

Accurate segmentation of medical images, particularly for anatomical structures with irregular shapes and low contrast such as the esophagus, remains a significant challenge. To address this issue, we propose MEM-UNet, a robust 3D Mamba-based UNet framework enhanced by mathematical morphology. Our approach adapts the State Space Model (SSM) in Mamba to support three-dimensional CT volumes, establishing an effective 3D perception backbone for the UNet architecture. In addition, we incorporate Morphology-Aware Spatial-Channel Attention (MASCA) blocks into the skip connections, where Morphology-Enhanced Spatial Convolution (MESC) augments spatial representations while Squeeze-and-Excitation (SE) highlights channel- wise features. This integration effectively leverages the shape-awareness provided by morphological operations, thus improving boundary precision. To further refine segmentation, we introduce a Morphology-Enhanced Decision (MED) layer that sharpens contour boundaries and performs voxel-level classification with high precision. Extensive experiments on SegTHOR and BTCV datasets demonstrate that MEM-UNet surpasses state-of-the-art models, achieving Dice Similarity Coefficient (DSC) scores of 87.42% and 74.86% for multi-organ segmentation, and 78.94% and 67.70% for esophagus segmentation, respectively. Ablation studies confirm the effectiveness of the proposed components and highlight the benefits of integrating mathematical morphology into our pipeline. The implementation is available at https://gitfront.io/r/cheee123/DDTJhrf3LRMd/MEM-UNet/.