MCCDNet: multi-level collaborative contrast decoupling medical image segmentation network

The aim of this study is to address the problem of low segmentation accuracy in medical image analysis caused by blurred tumour boundaries. We propose a Multi-level Collaborative Contrast Decoupling Network (MCCDNet) for medical image segmentation (MIS). Initially, a contrast consistency enhancement network is constructed to extract complex texture and detail features of tumour lesion boundaries. This ensures that the extracted features can adapt to complex environments. Secondly, we inject the prior knowledge into the multi-level contrast decoupling network to guide further training, enabling the model to capture the key features of the lesion area more effectively. Then, local-global attention module (LGAM) and gated bottleneck convolution (GBC) are used to further refine feature extraction, enhance the model’s focus on key regions, and reduce the uncertainty in the segmentation process. Ultimately, we propose using contrast feature-guided aggregation (CFGA) technology to fuse and enhance multi-level, refined features. This will improve the accuracy of the model in segmenting lesion regions within complex medical images. The experimental results demonstrate that MCCDNet achieves DSC scores of 96.09, 92.08, and 89.29 on the PROMISE12, ISIC2018, and Glas medical image datasets, respectively. It can provide auxiliary reference for precise quantification of clinical lesions and treatment planning.