提出高效三维医学图像分割模型，兼顾细节与全局上下文。
BiSegMamba: Efficient Bidirectional Tri-Oriented Mamba for 3D Medical Image Segmentation
- 三方向双向序列化+共享Mamba，提升长程依赖建模效率
- 参数减少65.9%，推理延迟降53.6%，吞吐量提升115.7%
- 适用于对精度与速度要求高的临床三维影像分析
精准的3D医学图像分割需兼顾精细空间细节与长程体素上下文。尽管Mamba能高效建模长序列，现有体素形式仍受限于空间序列顺序敏感、多方向重复处理及固定方向特征聚合。本文提出BiSegMamba，一种高效双向三向Mamba框架，遵循由简至繁、由局部到全局的处理策略。渐进式压缩主干将语义推理转移至紧凑表示，同时保留浅层高分辨率特征用于重建；多尺度空间混合器捕捉局部解剖模式。全局上下文建模中，体素特征沿三个解剖方向及其反向序列化，生成六条序列由共享Mamba算子联合处理，并通过学习的通道级前向-后向加权进行融合。最终通过输入相关方向融合整合恢复的各向特征。在自研颈动脉数据集及ACDC、BraTS2023、AMOS-CT上，Dice分数分别为96.3%、91.63%、92.57%、89.03%。相较于标准$128^3$配置下的SegMamba-V2，BiSegMamba参数量从138.77M降至47.38M（减少65.9%），推理延迟由129.08ms降至59.86ms（降低53.6%），吞吐量从7.75样本/s提升至16.71样本/s（增加115.7%）。结果表明该模型在体素Mamba分割中实现了更优的精度-效率权衡。
原文摘要 · Abstract (English)
Accurate 3D medical image segmentation requires both fine spatial detail and long-range volumetric context. Although Mamba provides efficient long-sequence modeling, volumetric formulations remain sensitive to spatial serialization order, repeated multi-orientation processing, and fixed aggregation of orientation-specific representations. We propose BiSegMamba, an efficient bidirectional tri-oriented Mamba framework that follows a compact-to-detail and local-to-global processing strategy. A progressive compacting stem shifts semantic reasoning to compact representations while retaining shallow high-resolution features for reconstruction, and a multi-scale spatial mixer captures local anatomical patterns. For global context modeling, volumetric features are serialized along three anatomical orientations and their reversed directions, the resulting six sequences are processed jointly by a shared Mamba operator, and reconciled using learned channel-wise forward-backward weighting. The restored orientation-specific features are subsequently integrated through input-dependent directional fusion. Experiments on an in-house carotid dataset and ACDC, BraTS2023, and AMOS-CT achieve Dice scores of 96.3%, 91.63%, 92.57%, and 89.03%, respectively. Compared with SegMamba-V2 under standardized $128^3$ profiling, BiSegMamba reduces the parameter count by 65.9% (138.77M$\rightarrow$47.38M) and inference latency by 53.6% (129.08$\rightarrow$59.86 ms), while increasing throughput by 115.7% (7.75$\rightarrow$16.71 samples/s). These results demonstrate a favorable accuracy--efficiency trade-off among volumetric Mamba-based segmentation models.
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