  Medical image segmentation is essential in diagnostics, treatment planning,
and healthcare, with deep learning offering promising advancements. Notably,
the convolutional neural network (CNN) excels in capturing local image
features, whereas the Vision Transformer (ViT) adeptly models long-range
dependencies through multi-head self-attention mechanisms. Despite their
strengths, both the CNN and ViT face challenges in efficiently processing
long-range dependencies in 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 study introduces
Semi-Mamba-UNet, which integrates a purely visual Mamba-based U-shaped
encoder-decoder architecture with a conventional CNN-based UNet into a
semi-supervised learning (SSL) framework. This innovative SSL approach
leverages both networks to generate pseudo-labels and cross-supervise one
another at the pixel level simultaneously, drawing inspiration from consistency
regularisation techniques. Furthermore, we introduce a self-supervised
pixel-level contrastive learning strategy that employs a pair of projectors to
enhance the feature learning capabilities further, especially on unlabelled
data. Semi-Mamba-UNet was comprehensively evaluated on two publicly available
segmentation dataset and compared with seven other SSL frameworks with both
CNN- or ViT-based UNet as the backbone network, highlighting the superior
performance of the proposed method. The source code of Semi-Mamba-Unet, all
baseline SSL frameworks, the CNN- and ViT-based networks, and the two
corresponding datasets are made publicly accessible.
