Medical image segmentation is essential in diagnostics, treatment planning,
and healthcare, with deep learning offering promising advancements. Notably,
Convolutional Neural Network (CNN) excel in capturing local image features,
whereas Vision Transformer (ViT) adeptly model long-range dependencies through
multi-head self-attention mechanisms. Despite their strengths, both CNN and ViT
face challenges in efficiently processing long-range dependencies within
medical images, often requiring substantial computational resources. This
issue, combined with the high cost and limited availability of expert
annotations, poses significant obstacles to achieving precise segmentation. To
address these challenges, this paper introduces the Semi-Mamba-UNet, which
integrates a visual mamba-based UNet architecture with a conventional UNet into
a semi-supervised learning (SSL) framework. This innovative SSL approach
leverages dual networks to jointly generate pseudo labels and cross supervise
each other, drawing inspiration from consistency regularization techniques.
Furthermore, we introduce a self-supervised pixel-level contrastive learning
strategy, employing a projector pair to further enhance feature learning
capabilities. Our comprehensive evaluation on a publicly available MRI cardiac
segmentation dataset, comparing against various SSL frameworks with different
UNet-based segmentation networks, highlights the superior performance of
Semi-Mamba-UNet. The source code has been made publicly accessible.

利用 Semi-Mamba-UNet 结合了可视 mamba-based UNet 架构和常规 UNet，采用半监督学习（SSL）框架来解决医学影像分割中的长程依赖和专家标注问题，并通过自监督像素级对比学习策略提升特征学习性能。