Convolutional Neural Networks (CNNs) and Transformers have been the most popular architectures for biomedical image segmentation, but both of them have limited ability to handle long-range dependencies because of inherent locality or computational complexity. To address this challenge, we introduce U-Mamba, a general-purpose network for biomedical image segmentation. Inspired by the State Space Sequence Models (SSMs), a new family of deep sequence models known for their strong capability in handling long sequences, we design a hybrid CNN-SSM block that integrates the local feature extraction power of convolutional layers with the abilities of SSMs for capturing the long-range dependency. Moreover, U-Mamba enjoys a self-configuring mechanism, allowing it to automatically adapt to various datasets without manual intervention. We conduct extensive experiments on four diverse tasks, including the 3D abdominal organ segmentation in CT and MR images, instrument segmentation in endoscopy images, and cell segmentation in microscopy images. The results reveal that U-Mamba outperforms state-of-the-art CNN-based and Transformer-based segmentation networks across all tasks. This opens new avenues for efficient long-range dependency modeling in biomedical image analysis. The code, models, and data are publicly available at https://wanglab.ai/u-mamba.html.

翻译：卷积神经网络（CNN）与Transformer一直是生物医学图像分割领域最主流的架构，但由于固有的局部性限制或计算复杂性，两者在处理长程依赖关系方面能力有限。为解决这一挑战，我们提出了U-Mamba——一种适用于生物医学图像分割的通用网络。受状态空间序列模型（SSMs）这一以强大长序列处理能力著称的新型深度序列模型的启发，我们设计了混合CNN-SSM模块，该模块融合了卷积层提取局部特征的能力与SSMs捕捉长程依赖的能力。此外，U-Mamba具备自配置机制，能够自动适应不同数据集而无需人工干预。我们在四项不同任务上进行了广泛实验，包括CT和MR图像中的三维腹部器官分割、内窥镜图像中的器械分割以及显微图像中的细胞分割。结果表明，U-Mamba在所有任务中均优于最先进的基于CNN和基于Transformer的分割网络。这为生物医学图像分析中的高效长程依赖建模开辟了新途径。代码、模型及数据已在https://wanglab.ai/u-mamba.html公开。