Cross-layer semantic alignment and context enhancement network for medical image segmentation

Medical image segmentation aims to accurately delineate organs, tissues, or lesion regions from complex medical images. However, existing hybrid models based on Transformers and convolutional neural networks still suffer from limitations in local detail modeling and cross-layer feature fusion, which often leads to blurred boundary information and loss of structural details. To address these issues, this paper proposes a Cross-layer Semantic Alignment and Context Enhancement Network for medical image segmentation. Specifically, a semantic enhancement module is introduced into the skip connections to achieve effective fusion of high-level semantic information and shallow spatial details through spatial-channel collaborative modeling and multi-scale context extraction (MCE). In addition, a lightweight boundary refinement mechanism is employed in the decoder stage to improve the recovery capability for complex boundary regions. Experiments conducted on the Synapse, ACDC, and GlaS datasets demonstrate that the proposed method outperforms mainstream approaches in terms of Dice, HD95, and Intersection over Union metrics, validating the effectiveness of the cross-layer semantic alignment mechanism for complex medical image segmentation tasks.