SFAG-DeepLabv3+: An automatic segmentation approach for coronary angiography images

Automated segmentation of coronary angiography images is highly significant for computer-aided diagnosis of coronary heart disease. However, existing segmentation methods suffer from the problem of poor segmentation results caused by insufficient extraction and fusion of the features of the complex topological structure of blood vessels. In view of this, this paper proposes an automated segmentation method for coronary angiography images based on SFAG-DeepLabv3+. This method utilizes the Swin Transformer network to screen coronary angiography images and proposes a Filtering Smoothing Equalization (FSE) image enhancement method to improve the quality of angiography images. Furthermore, this paper proposes an improved automatic segmentation network for coronary arteries based on the DeepLabv3+. In the encoder section, an Adaptive hybrid Dilated convolution and double Pooling (ADP) module is proposed to enhance the ability to extract topological features of coronary blood vessels. Between the encoder and decoder, a Gaussian Context Spatial Fusion (GCSF) module is proposed to reduce information loss during the compression and decompression of information from the encoder to the decoder. In the decoder section, bicubic interpolation upsampling is employed to improve the continuity of the segmented blood vessel topology. To validate the effectiveness of the proposed method, experiments were conducted using both the ARCADE public dataset and a self-constructed CSH dataset. Experimental results demonstrate that the method proposed in this paper can perform effective feature extraction, fusion and correction on coronary angiography images, achieving average Dice coefficients of 0.9249 on the CSH dataset and 0.9156 on the ARCADE dataset.