Deep Learning-Based Segmentation and Localization in CT Angiography for Coronary Heart Disease Diagnosis

The most prevalent cause of mortality worldwide is coronary artery disease (CAD), and coronary angiography is the gold standard for its diagnosis. There are still a limited number of rapid and efficient automated methods for measuring the disease and localizing the coronary anatomy, even though over 2 million procedures are performed annually globally. This study proposes a deep learning-based framework for diagnosing coronary heart disease through segmentation and localization, called multi-stream attention-guided coronary analysis network (MACAN). The proposed MACAN employs a dual-stream model combining segmentation and localization tasks, enhanced by channel and spatial attention modules, to improve feature extraction and accuracy. During data pre-processing, several enhancement techniques are employed to augment image quality. The proposed model incorporates dual-stream processing with channel and spatial attention mechanisms to improve feature extraction. The automatic region-based coronary artery disease diagnostics using the x-ray angiography images (ARCADE) dataset was utilized for model training and evaluation. The proposed model achieved a classification accuracy of 0.6154 and 0.5827 on validation sets of phase 1 and phase 2, whereas it achieved F1-scores of 0.4287 and 0.4126 on validation sets of phase 1 and phase 2, respectively. The proposed MACAN model is compared with state-of-the-art methods, including FPN, PAN, DeepLabV3, UNet++ and Ensemble model, where the F1-score remains between 18% to 39%. In contrast, the proposed model achieved 43% F1-score or phase 1 validation data and 41% on phase 2 validation data. This study demonstrates the capability of automated technologies to improve the precision of stent injections in clinical environments, facilitate treatments, and aid in identifying CAD.