MambaVesselNet++：一种用于医学图像分割的混合CNN-Mamba架构
MambaVesselNet++: A Hybrid CNN-Mamba Architecture for Medical Image Segmentation
AI总结：
针对医学图像分割中传统卷积架构感受野受限、视觉Transformer计算成本高的问题，提出融合CNN与Mamba的MambaVesselNet++框架，在多类医学分割任务上性能超越现有最优方法。
摘要：
医学图像分割在计算机辅助诊断中发挥着重要作用。传统的基于卷积的U形分割架构通常受限于局部感受野。现有的视觉Transformer凭借其出色的全局上下文捕获能力，已被广泛应用于各类医学分割框架中。尽管具备这一优势，视觉Transformer的实际应用仍受其非线性自注意力机制的制约，该机制需要高昂的计算成本。为解决这一问题，选择性状态空间模型（SSM）Mamba因擅长建模序列数据中的长程依赖关系，且以高效的内存开销著称，获得了广泛认可。本文提出了MambaVesselNet++，一种用于医学图像分割的混合CNN-Mamba框架。我们的MambaVesselNet++由混合图像编码器（Hi-Encoder）和双焦融合解码器（BF-Decoder）组成。在Hi-Encoder中，我们首先设计了纹理感知层，利用卷积捕获低层语义特征。随后，我们使用Mamba以线性复杂度有效建模长程依赖关系。Bi-Decoder采用跳跃连接融合Hi-Encoder的局部与全局信息，以精准生成分割掩码。大量实验表明，在各类医学2D、3D以及实例分割任务上，MambaVesselNet++的性能优于当前基于卷积、基于Transformer以及基于Mamba的最优方法。代码可在https://github.com/CC0117/MambaVesselNet获取。
英文摘要：
Medical image segmentation plays an important role in computer-aided diagnosis. Traditional convolution-based U-shape segmentation architectures are usually limited by the local receptive field. Existing vision transformers have been widely applied to diverse medical segmentation frameworks due to their superior capabilities of capturing global contexts. Despite the advantage, the real-world application of vision transformers is challenged by their non-linear self-attention mechanism, requiring huge computational costs. To address this issue, the selective state space model (SSM) Mamba has gained recognition for its adeptness in modeling long-range dependencies in sequential data, particularly noted for its efficient memory costs. In this paper, we propose MambaVesselNet++, a Hybrid CNN-Mamba framework for medical image segmentation. Our MambaVesselNet++ is comprised of a hybrid image encoder (Hi-Encoder) and a bifocal fusion decoder (BF-Decoder). In Hi-Encoder, we first devise the texture-aware layer to capture low-level semantic features by leveraging convolutions. Then, we utilize Mamba to effectively model long-range dependencies with linear complexity. The Bi-Decoder adopts skip connections to combine local and global information of the Hi-Encoder for the accurate generation of segmentation masks. Extensive experiments demonstrate that MambaVesselNet++ outperforms current convolution-based, transformer-based, and Mamba-based state-of-the-arts across diverse medical 2D, 3D, and instance segmentation tasks. The code is available at https://github.com/CC0117/MambaVesselNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
