U-Net Model Based on Cbam Attention Mechanism for Coronary Angiography Segmentation

Coronary artery disease is a prominent contributor to cardiovascular death. Automatic segmentation of Coronary Angiography (CAG) images is crucial for early diagnosis and treatment and holds significant importance in clinical diagnosis, surgical planning, and treatment evaluation. However, due to challenges such as poor image quality, complex backgrounds, and fine vessel structures, the task of automatic segmentation has always been difficult. This study aims to improve the accuracy and robustness of automatic CAG segmentation. Therefore, we propose a UNet model improved with the Convolutional Block Attention Module (CBAM), which enhances the model’s ability to capture important feature areas by combining the Channel Attention Module (CAM) and the Spatial Attention Module (SAM), making the identified vessels completer and more accurate. Specifically, the CBAM module is integrated into each convolutional layer to enhance the feature representation capabilities of the channel and space. We evaluate the CBAM-UNet based on X-ray Angiography Coronary Artery Disease (XCAD) datasets. The experimental results indicate that the improved model’s accuracy increased to 97%, and precision reached 89.95%. The results demonstrate that the improved model’s overall performance on various metrics surpasses traditional methods, with significant improvements in anti-background noise interference and handling complex vascular structures.