In the field of medical image segmentation, variant models based on Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs) as the base modules have been very widely developed and applied. However, CNNs are often limited in their ability to deal with long sequences of information, while the low sensitivity of ViTs to local feature information and the problem of secondary computational complexity limit their development. Recently, the emergence of state-space models (SSMs), especially 2D-selective-scan (SS2D), has had an impact on the longtime dominance of traditional CNNs and ViTs as the foundational modules of visual neural networks. In this paper, we extend the adaptability of SS2D by proposing a High-order Vision Mamba UNet (H-vmunet) for medical image segmentation. Among them, the proposed High-order 2D-selective-scan (H-SS2D) progressively reduces the introduction of redundant information during SS2D operations through higher-order interactions. In addition, the proposed Local-SS2D module improves the learning ability of local features of SS2D at each order of interaction. We conducted comparison and ablation experiments on three publicly available medical image datasets (ISIC2017, Spleen, and CVC-ClinicDB), and the results all demonstrate the strong competitiveness of H-vmunet in medical image segmentation tasks. The code is available from https://github.com/wurenkai/H-vmunet .

翻译：在医学图像分割领域，基于卷积神经网络（CNN）和视觉Transformer（ViT）作为基础模块的变体模型已得到广泛开发与应用。然而，CNN在处理长序列信息时能力受限，而ViT对局部特征信息的低敏感性以及二次计算复杂度问题限制了其发展。近年来，状态空间模型（SSM）尤其是二维选择性扫描（SS2D）的出现，对传统CNN和ViT长期主导视觉神经网络基础模块的地位产生了冲击。本文通过提出一种用于医学图像分割的高阶视觉Mamba UNet（H-vmunet），扩展了SS2D的适应性。其中，所提出的高阶二维选择性扫描（H-SS2D）通过高阶交互逐步减少SS2D操作中冗余信息的引入。此外，所提出的Local-SS2D模块在每个阶次交互中提升了SS2D对局部特征的学习能力。我们在三个公开医学图像数据集（ISIC2017、Spleen和CVC-ClinicDB）上进行了对比实验和消融实验，结果均表明H-vmunet在医学图像分割任务中具有强大的竞争力。代码可从https://github.com/wurenkai/H-vmunet获取。