REMC-UNet: A residual enhanced Mamba-CNN UNet for pancreas segmentation

Abstract Pancreas segmentation in CT images is fundamental for subsequent diagnosis and qualitative treatment of pancreatic cancer. Since the morphology of the pancreas may be influenced by issues such as class imbalance and boundary blurring across different individuals, segmenting the pancreas from abdominal CT images is a challenging task. To address these issues, this paper proposes a novel pancreas CT image segmentation network, REMC-UNet. First, we introduce the Residual Visual State Space block (ResVSS block) to capture extensive contextual information and effectively extract key features from pancreas CT images. Additionally, we design the Multi-Scale Hybrid Attention (MSHA) to aggregate long-range dependencies and leverage multi-scale spatial information to address the issue of unclear pancreatic boundaries. Finally, we propose the Feature Enhancement block (FE block), which allows the model to focus on global features while also attending to local regions during the feature recovery process. Through experiments on the public NIH dataset, we achieve an average Dice Similarity Coefficient (DSC) of 86.64 ± 4.32%, improving by 2.73% over the baseline model and outperforming other segmentation models.