arXiv 2402.024912024-11-08eess.IVcs.CV

VM-UNet：用于医学图像分割的Vision Mamba UNet

VM-UNet: Vision Mamba UNet for Medical Image Segmentation

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）

Jiacheng Ruan, Jincheng Li, Suncheng Xiang

备注：: 9 pages, 5 figures, 6 tables. Work in progress

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AI总结：

针对医学图像分割，提出基于状态空间模型的VM-UNet，采用VSS块和不对称编码器-解码器结构，在ISIC17、ISIC18和Synapse数据集上表现竞争力，为SSM分割系统建立基线。

摘要：

在医学图像分割领域，基于CNN和基于Transformer的模型已被广泛探索。然而，CNN在长距离建模能力方面存在局限性，而Transformer则受限于其二次方计算复杂度。最近，以Mamba为代表的状态空间模型（SSMs）已成为一种有前景的方法。它们不仅在建模长距离交互方面表现出色，而且保持了线性计算复杂度。在本文中，利用状态空间模型，我们提出了一种用于医学图像分割的U形架构模型，命名为Vision Mamba UNet（VM-UNet）。具体而言，引入了视觉状态空间（VSS）块作为基础块以捕获广泛的上下文信息，并构建了具有较少卷积层的不对称编码器-解码器结构以节省计算成本。我们在ISIC17、ISIC18和Synapse数据集上进行了全面实验，结果表明VM-UNet在医学图像分割任务中表现具有竞争力。据我们所知，这是第一个基于纯SSM模型构建的医学图像分割模型。我们旨在为未来开发更高效、更有效的基于SSM的分割系统建立基线和提供有价值的见解。我们的代码可在https://github.com/JCruan519/VM-UNet获取。

英文摘要：

In the realm of medical image segmentation, both CNN-based and Transformer-based models have been extensively explored. However, CNNs exhibit limitations in long-range modeling capabilities, whereas Transformers are hampered by their quadratic computational complexity. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as a promising approach. They not only excel in modeling long-range interactions but also maintain a linear computational complexity. In this paper, leveraging state space models, we propose a U-shape architecture model for medical image segmentation, named Vision Mamba UNet (VM-UNet). Specifically, the Visual State Space (VSS) block is introduced as the foundation block to capture extensive contextual information, and an asymmetrical encoder-decoder structure is constructed with fewer convolution layers to save calculation cost. We conduct comprehensive experiments on the ISIC17, ISIC18, and Synapse datasets, and the results indicate that VM-UNet performs competitively in medical image segmentation tasks. To our best knowledge, this is the first medical image segmentation model constructed based on the pure SSM-based model. We aim to establish a baseline and provide valuable insights for the future development of more efficient and effective SSM-based segmentation systems. Our code is available at https://github.com/JCruan519/VM-UNet.

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。