用双马比结构提升3D医学图像分割精度，兼顾全局上下文与解剖结构保留。
DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling
- 设计四向扫描的3D马比模块，保持解剖空间一致性。
- 在腹部器官和脑肿瘤分割上分别达到85.44%和90.22%的最高分割精度。
- 适合需要高精度分割的临床医学影像分析场景。
精准的3D医学图像分割需要能协调全局上下文建模与空间拓扑保持的架构。尽管状态空间模型（SSMs）如Mamba在序列建模中展现潜力，现有医疗领域的SSMs存在编码器-解码器不兼容问题：编码器的1D序列展平破坏了空间结构，而传统解码器无法利用Mamba的状态传播。我们提出DM-SegNet，一种融合方向性状态转移与解剖感知分层解码的双马比架构。核心创新包括：采用四向3D扫描的四向空间马比模块以维持解剖空间连贯性、门控空间卷积层在状态建模前增强空间敏感特征表示、以及基于马比的解码框架实现跨尺度双向状态同步。在两个临床重要基准上的广泛评估表明，该方法效果显著：在Synapse数据集上对腹部器官分割的Dice相似系数（DSC）达到85.44%，在BraTS2023数据集上对脑肿瘤分割的DSC达到90.22%。
原文摘要 · Abstract (English)
Accurate 3D medical image segmentation demands architectures capable of reconciling global context modeling with spatial topology preservation. While State Space Models (SSMs) like Mamba show potential for sequence modeling, existing medical SSMs suffer from encoder-decoder incompatibility: the encoder's 1D sequence flattening compromises spatial structures, while conventional decoders fail to leverage Mamba's state propagation. We present DM-SegNet, a Dual-Mamba architecture integrating directional state transitions with anatomy-aware hierarchical decoding. The core innovations include a quadri-directional spatial Mamba module employing four-directional 3D scanning to maintain anatomical spatial coherence, a gated spatial convolution layer that enhances spatially sensitive feature representation prior to state modeling, and a Mamba-driven decoding framework enabling bidirectional state synchronization across scales. Extensive evaluation on two clinically significant benchmarks demonstrates the efficacy of DM-SegNet: achieving state-of-the-art Dice Similarity Coefficient (DSC) of 85.44% on the Synapse dataset for abdominal organ segmentation and 90.22% on the BraTS2023 dataset for brain tumor segmentation.
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