Abstract Medical image segmentation plays a vital role in diagnosis, treatment planning, and disease monitoring. However, endoscopic and dermoscopic images often exhibit blurred boundaries and low contrast, presenting a significant challenge for precise segmentation. Moreover, single encoder–decoder architectures suffer from inherent limitations, resulting in the loss of either fine-grained details or global context. Some dual-encoder models yield inaccurate results due to mismatched receptive fields and overly simplistic fusion strategies. To overcome these issues, we present the Dual Backbone Cascaded Multi-Convolutional Segmentation Network (DBCM-Net). Our approach employs a Multi-Axis Vision Transformer and a Vision Mamba encoder to extract semantic features at multiple scales, with a cascaded design that enables information sharing between the two backbones. We introduce the Global and Local Fusion Attention Block (GLFAB) to generate attention masks that seamlessly integrate global context with local detail, producing more precise feature maps. Additionally, we incorporate a Depthwise Separable Convolution Attention Module (DSCAM) within the encoders to strengthen the model’s ability to capture critical features. A Feature Refinement Fusion Block (FRFB) is further applied to refine these feature maps before subsequent processing. The cascaded network architecture synergistically combines the complementary strengths of both encoders. We rigorously evaluated our model on three distinct datasets, achieving Dice coefficients of 94.93% on the CVC-ClinicDB polyp dataset, 91.93% on ISIC2018, and 92.73% on ACDC, each surpassing current state-of-the-art methods. Extensive experiments demonstrate that the proposed method excels in segmentation accuracy and preserves edge details effectively.
Wang et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: