A combination network based on graph modules for cardiac segmentation

Background Cardiac segmentation plays a crucial role in extracting cardiac substructures from computed tomography images, providing necessary information for subsequent cardiac disease diagnostics. Current research on cardiac segmentation algorithms faces challenges such as complex spatial distributions and limited labeled samples. The preferred method, convolutional neural network (CNN), excels at capturing features at various levels. However, the coarse convolutional operations of CNN often neglect valuable structural information. In contrast, graph neural network (GNN) excels at capturing complex spatial relationships among nodes by aggregating messages from graphs. Methodology In this work, a GNN-CNN combination network (GCCN) with enhanced feature extraction by GNN is proposed for cardiac segmentation. The combination network integrates the strengths of two approaches while compensating for each other's shortcomings. Firstly, 3D CT images are transformed into graphs, whose nodes with similar feature values within a certain spatial proximity are connected. Secondly, a lightweight graph attention network (LGAT) module is proposed for aggregating the messages of constructed graphs. Thirdly, the data, resampled from the feature-enhanced graphs, is fed into the 3D ResU-Net for the final segmentation process. Result The proposed method achieves an average Dice score of 92.98% which is 1.84% higher than 3D ResU-Net. The average Dice score of each cardiac substructure improved, particularly for the ascending aorta, achieving 96.35%, an 8.29% improvement over 3D ResU-Net. Additionally, our study demonstrates that GCCN has the improved adaptability in handling datasets with limited samples. Conclusion In general, GCCN allows for the extraction of richer structural information from CT images, resulting in stronger feature representation of the data. Compared to pure convolutional methods, GCCN not only enhances segmentation accuracy but also exhibits greater adaptability to small-sample data. GCCN represents an exploration of applying graph neural networks to cardiac segmentation tasks from a novel perspective.