MambaClinix：基于层级门控卷积与Mamba的U-Net用于增强三维医学图像分割
MambaClinix: Hierarchical Gated Convolution and Mamba-Based U-Net for Enhanced 3D Medical Image Segmentation
AI总结：
提出MambaClinix，一种结合层级门控卷积与Mamba的U形架构，用于三维医学图像分割，在五个基准数据集上实现高精度与低复杂度。
摘要：
深度学习，特别是卷积神经网络（CNNs）和Transformer，已显著推进了三维医学图像分割的发展。虽然CNN在捕捉局部特征方面非常有效，但其有限的感受野可能在复杂临床场景中阻碍性能。相比之下，Transformer擅长建模长距离依赖，但计算密集，导致训练和部署成本高昂。最近，基于状态空间模型（SSM）的Mamba架构被提出，以在线性计算复杂度下高效建模长距离依赖。然而，其在医学图像分割中的应用暴露出不足，特别是在捕捉准确描绘临床区域所必需的关键局部特征方面。在本研究中，我们提出了MambaClinix，一种新颖的U形医学图像分割架构，它将层级门控卷积网络（HGCN）与Mamba集成在自适应阶段式框架中。该设计显著提高了计算效率和高阶空间交互，使模型能够有效捕捉医学图像中的近端和远端关系。具体而言，我们的HGCN旨在通过纯卷积结构模拟Transformer的注意力机制，促进特征图中的高阶空间交互，同时避免与基于Transformer的方法相关的计算复杂度。此外，我们引入了一种区域特定的Tversky损失，该损失强调特定像素区域以改善自动分割性能，从而优化模型的决策过程。在五个基准数据集上的实验结果表明，所提出的MambaClinix在保持低模型复杂度的同时实现了高分割精度。
英文摘要：
Deep learning, particularly convolutional neural networks (CNNs) and Transformers, has significantly advanced 3D medical image segmentation. While CNNs are highly effective at capturing local features, their limited receptive fields may hinder performance in complex clinical scenarios. In contrast, Transformers excel at modeling long-range dependencies but are computationally intensive, making them expensive to train and deploy. Recently, the Mamba architecture, based on the State Space Model (SSM), has been proposed to efficiently model long-range dependencies while maintaining linear computational complexity. However, its application in medical image segmentation reveals shortcomings, particularly in capturing critical local features essential for accurate delineation of clinical regions. In this study, we propose MambaClinix, a novel U-shaped architecture for medical image segmentation that integrates a hierarchical gated convolutional network(HGCN) with Mamba in an adaptive stage-wise framework. This design significantly enhances computational efficiency and high-order spatial interactions, enabling the model to effectively capture both proximal and distal relationships in medical images. Specifically, our HGCN is designed to mimic the attention mechanism of Transformers by a purely convolutional structure, facilitating high-order spatial interactions in feature maps while avoiding the computational complexity typically associated with Transformer-based methods. Additionally, we introduce a region-specific Tversky loss, which emphasizes specific pixel regions to improve auto-segmentation performance, thereby optimizing the model's decision-making process. Experimental results on five benchmark datasets demonstrate that the proposed MambaClinix achieves high segmentation accuracy while maintaining low model complexity.
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