Jialin Zhang

The work proposes BiM-GeoAttn-Net, which cascades a Bidirectional Depth Mamba (BiM) and a Geometry-Aware Vessel Attention (GeoAttn) module at the nnU-Net bottleneck, and on 71 multi-source Stanford Type-B aortic dissection CTA cases performs binary segmentation of the vessel foreground (true plus false lumen), achieving Dice 93.35%, IoU 87.53%, Recall 94.85%, Precision 92.04%, and HD95 12.36 mm, outperforming Attention U-Net, nnU-Net, Swin-UNet, SegFormer3D, and Mamba-UNet on overlap metrics while keeping boundary accuracy and computational cost competitive.

The work proposes BiM-GeoAttn-Net, which cascades a Bidirectional Depth Mamba (BiM) and a Geometry-Aware Vessel Attention (GeoAttn) module at the nnU-Net bottleneck, and on 71 multi-source Stanford Type-B aortic dissection CTA cases performs binary segmentation of the vessel foreground (true plus false lumen), achieving Dice 93.35%, IoU 87.53%, Recall 94.85%, Precision 92.04%, and HD95 12.36 mm, outperforming Attention U-Net, nnU-Net, Swin-UNet, SegFormer3D, and Mamba-UNet on overlap metrics while keeping boundary accuracy and computational cost competitive.