###### Abstract  
Introduction: Ischemic heart disease represents one of the main causes of mortality and morbidity, requiring accurate, noninvasive imaging. Coronary Computed Tomography Angiography (CCTA) offers a detailed coronary assessment but can be labor-intensive and operator-dependent. Methods: We developed a bounding box-based object detection method using deep learning to identify the right coronary artery (RCA), left anterior descending artery (LCA-LAD), and left circumflex artery (LCA-CX) in the CCTA cross-sections. A total of 19,047 images, which were recorded from 52 patients, underwent a five-fold cross-validation. The evaluation metrics included Average Precision (AP), Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and Mean Absolute Error (MAE) to achieve both detection accuracy and spatial localization precision. Results: The mean AP scores for RCA, LCA-LAD, and LCA-CX were 0.71, 0.70, and 0.61, respectively. IoU and DSC indicated a better overlap for LCA-LAD, whereas LCA-CX was more challenging to detect. The MAE analysis showed the largest centroid deviation in RCA, highlighting variable performance across the artery classes. Discussion: These findings demonstrate the feasibility of automated coronary artery detection, potentially reducing observer variability and expediting CCTA analysis. They also highlight the need to refine the approach for complex anatomical variants or calcified plaques. Conclusion: A bounding box-based approach can thereby streamline clinical workflows by localizing major coronary arteries. Future research with diverse datasets and advanced visualization techniques may further enhance diagnostic accuracy and efficiency. cornary computed tomography angiography (ccta); object detection; deep learning 2020 IEEE/RSJ International Conference on Acoustics and Speech (  
coronary syndromes, and myocardial infarction, all of which have profound implications for patient outcomes. A central mechanism underlying these conditions is atherosclerosis, a progressive inflammatory process characterized by the accumulation of lipids, cholesterol, and fibrous elements within the arterial wall. Over time, atherosclerosis can lead to luminal narrowing in the coronary arteries, impairing myocardial perfusion and heightening the likelihood of ischemic events. Therefore, the timely detection and accurate quantification of coronary artery lesions are essential for guiding clinical decision-making and improving patient prognosis [1]. Over the past two decades, Coronary Computed Tomography Angiography (CCTA) has emerged as a key noninvasive imaging modality for the assessment of coronary artery disease (CAD). Compared to invasive coronary angiography, CCTA offers the advantage of visualizing both the coronary lumen and the arterial wall, enabling the early identification of plaque burden and the degree of luminal stenosis [2]. Advances in scanner technology, such as higher temporal and spatial resolution, have further improved the diagnostic accuracy of CCTA. Additionally, its relatively lower risk profile and broader accessibility in many clinical settings have encouraged wider adoption. By providing detailed anatomical information, CCTA can also reduce the need for unnecessary invasive procedures in individuals ultimately found to have nonobstructive or minimal disease, thereby optimizing resource utilization [3]. Nevertheless, substantial challenges remain in CCTA image processing and interpretation. The manual post-processing of CCTA datasets is both time-consuming and operator-dependent; approaches like maximum intensity projection (MIP) and curved planar reformation (CPR) [4; 5; 6; 7] can help but still produce artifacts due to vessel overlap and the complex three-dimensional structure of the coronary arteries.