Cardiac multi-structure segmentation network based on the fused dual attention mechanism.

Cardiac segmentation and quantification of cardiac function indicators play a crucial role in the clinical diagnosis and treatment of cardiovascular diseases. To address the issue of blurred cardiac chamber boundaries and adjacent tissue interference resulting from similar intensity in computed tomograph (CT) images, this paper proposes a 3D cardiac multi-structure segmentation network utilizing Multi-scale Channel Enhancement Attention (MCEA) and Spatial Decomposition with Channel Fusion Attention (SD-CA). The MCEA module integrates channel information from feature maps of various scales within the coding layer, thereby enhancing contextual linkage, strengthening the network's multi-scale feature representation capability, and improving decoding and segmentation performance. The SD-CA module generates spatial and channel attention weights in parallel and combines the three directional features of height, width, and depth. This enables the network to effectively concentrate on the region of interest and mitigate the interference of irrelevant structures. Experimental evaluations were conducted using a dataset of 192 cases provided by the People's Hospital of Liaoning Province and the MM-WHS dataset. Segmentation was achieved for the left ventricle, myocardium, left atrium, right ventricle, and right atrium, with average Dice coefficients of 94.21% and 93.9%, and average 95% Hausdorff distances of 6.5483 and 4.36, respectively. Furthermore, quantitative predictions of the left ventricular ejection fraction (LVEF) and substructure volumes were derived from the segmentation results. The correlation coefficients between the predicted and true values exceeded 0.9587, and all fell within the maximum error range of the Bland-Altman test for over 94.8% of the data, indicating a strong correlation and agreement between the predicted and true values.