MCHNet: An Efficient Cross Attention-Guided Hierarchical Multiscale Network for Segmentation of Organs at Risk in CT Images

In radiotherapy, precisely contoured organs at risk (OARs) near the target areas are essential for effective treatment planning. Manual delineation of OARs is labor-intensive and varies among experts. Deep learning has improved accuracy and consistency, but current methods use complex architectures with many parameters, risking overfitting on small medical image datasets. Addressing this, this article introduces MCHNet, a cross attention (CA)-guided hierarchical multiscale segmentation network based on CT images. MCHNet adopts a typical U-shaped structure, with MobileVit as its backbone. An efficient CA-guided block is utilized to enhance feature extraction while minimizing model parameters. Additionally, a novel skip-connection strategy is proposed to preserve critical medical image information during multiple down-sampling operations and bridge the gap between deep and shallow features. We conducted extensive experiments on three public datasets, i.e., multiatlas labeling beyond the cranial vault dataset, Segmentation of thoracic OARs dataset, and multimodality abdominal multiorgan segmentation challenge 2022 dataset. Experiment results demonstrate that the proposed method outperforms other related Transformer-based or hybrid models in terms of computational complexity, quantitative and qualitative results. We believe that the proposed method can offer an optimal blend of precision and efficiency that advances the capabilities of OARs segmentation in radiotherapy.