Automatic medical image segmentation technology has the potential to expedite pathological diagnoses, thereby enhancing the efficiency of patient care. However, medical images often have complex textures and structures, and the models often face the problem of reduced image resolution and information loss due to downsampling. To address this issue, we propose HC-Mamba, a new medical image segmentation model based on the modern state space model Mamba. Specifically, we introduce the technique of dilated convolution in the HC-Mamba model to capture a more extensive range of contextual information without increasing the computational cost by extending the perceptual field of the convolution kernel. In addition, the HC-Mamba model employs depthwise separable convolutions, significantly reducing the number of parameters and the computational power of the model. By combining dilated convolution and depthwise separable convolutions, HC-Mamba is able to process large-scale medical image data at a much lower computational cost while maintaining a high level of performance. We conduct comprehensive experiments on segmentation tasks including organ segmentation and skin lesion, and conduct extensive experiments on Synapse, ISIC17 and ISIC18 to demonstrate the potential of the HC-Mamba model in medical image segmentation. The experimental results show that HC-Mamba exhibits competitive performance on all these datasets, thereby proving its effectiveness and usefulness in medical image segmentation.

翻译：自动医学图像分割技术有潜力加速病理诊断，从而提升患者护理效率。然而，医学图像通常具有复杂的纹理和结构，模型常因下采样面临图像分辨率降低与信息丢失的问题。针对这一问题，我们提出HC-Mamba，一种基于现代状态空间模型Mamba的新型医学图像分割模型。具体而言，我们在HC-Mamba模型中引入扩张卷积技术，通过扩展卷积核的感受野，在不增加计算成本的情况下捕获更广泛的上下文信息。此外，HC-Mamba模型采用深度可分离卷积，显著减少了模型参数数量与计算量。通过结合扩张卷积与深度可分离卷积，HC-Mamba能够以更低计算成本处理大规模医学图像数据，同时保持高性能。我们在器官分割和皮肤病变分割任务上进行了全面实验，并在Synapse、ISIC17和ISIC18数据集上开展广泛测试，以证明HC-Mamba模型在医学图像分割中的潜力。实验结果表明，HC-Mamba在所有数据集上均展现出竞争性表现，从而验证了其在医学图像分割中的有效性与实用性。