MSMCNet: Differential context drives accurate localization and edge smoothing of lesions for medical image segmentation

Abstract

Medical image segmentation plays a crucial role in clinical assistance for diagnosis. The UNet-based network architecture has achieved tremendous success in the field of medical image segmentation. However, most methods commonly employ element-wise addition or channel merging to fuse features, resulting in smaller differentiation of feature information and excessive redundancy. Consequently, this leads to issues such as inaccurate lesion localization and blurred boundaries in segmentation. To alleviate these problems, the Multi-scale Subtraction and Multi-key Context Conversion Networks (MSMCNet) are proposed for medical image segmentation. Through the construction of differentiated contextual representations, MSMCNet emphasizes vital information and achieves precise medical image segmentation by accurately localizing lesions and enhancing boundary perception. Specifically, the construction of differentiated contextual representations is accomplished through the proposed Multi-scale Non-crossover Subtraction (MSNS) module and Multi-key Context Conversion Module (MCCM). The MSNS module utilizes the context of MCCM coding and redistribute the value of feature map pixels. Extensive experiments were conducted on widely used public datasets, including the ISIC-2018 dataset, COVID-19-CT-Seg dataset, Kvasir dataset, as well as a privately constructed traumatic brain injury dataset. The experimental results demonstrated that our proposed MSMCNet outperforms state-of-the-art medical image segmentation methods across different evaluation metrics.