## 4 Discussion  
This study employed a bounding box-based object detection approach to identify and localize coronary arteries (RCA, LCA-LAD, and LCA-CX) in CCTA images, building on the foundations laid out in our Introduction, Materials and Methods, and Results Sections. By highlighting the arterial lumen on cross-sectional slices, our model aimed to simplify a process that has traditionally been time-consuming and operator-dependent. Although the existing object detection literature often focuses on metrics such as Average Precision (AP), our study revealed that a broader set of metrics (IoU Above Threshold Rates, DSC Above Threshold Rates, and MAE) offers deeper insights. A major finding was the varying performance across the three arterial classes. The LCA-LAD consistently showed stronger overlap-based metrics (IoU and DSC), suggesting that it is more conspicuous on the CCTA cross-sections. However, the LCA-CX exhibited lower mean IoU and DSC values, implying that its anatomical course around the mitral valve annulus, along with a complex branching pattern, introduces challenges during model training and inference. Notably, the LCA-CX often displayed relatively low MAE values in some folds, indicating that, when identified, the center of its bounding box tended to be aligned precisely on the arterial lumen. These class-dependent differences  
Figure 3: (**a**–**h**) show axial views of CCTA images. (**a**–**h**) display the corresponding bounding boxes for the ground truth (yellow dashed lines with yellow filling) and inference results (RCA: cyan, LCA-LAD: magenta, LCA-CX: green). The center points of the bounding boxes are marked with yellow circles for the ground truth and green “\(\times\)” marks for the inference results. The yellow arrow highlights a relatively faint vessel segment that was missed by the detection model despite being annotated as ground truth. The red arrow indicates a calcified lesion with high CT attenuation value that was accurately detected at the vessel center. underscore the importance of using multiple metrics rather than relying on AP alone. While direct comparisons with voxel-based segmentation approaches are difficult due to differing annotation protocols, our measured DSC is essentially the same as the DSC = 0.74 reported in [31]. This finding suggests that our bounding box-based method achieves a comparable overlap accuracy to segmentation-based studies, even though the methodologies differ. From a clinical perspective, these findings are significant. Coronary artery disease continues to be a leading cause of morbidity and mortality worldwide, necessitating the accurate interpretation of CCTA images for risk stratification and treatment planning [32]. Recent studies have further emphasized the importance of automated image analysis in improving clinical workflow efficiency and diagnostic accuracy [24; 33]. By providing automated bounding boxes that highlight candidate arterial cross-sections, the proposed approach can streamline interpretation, reduce the time radiologists spend on routine tasks, and potentially improve diagnostic consistency. Although our bounding box-based approach has the potential to reduce inter-operator variability in identifying coronary arteries, the level of expertise required to acquire CCTA images remains crucial. Trained personnel are needed to ensure proper gating, contrast administration, and artifact minimization. Therefore, automation in post-processing does not eliminate the need for skilled operators but rather complements their efforts to enhance overall workflow efficiency. One unique strength of the bounding box-based methodology, as opposed to voxel-wise or pixel-wise segmentation approaches, is that it naturally avoids merging arteries and veins when their attenuation or morphology is ambiguous. Bounding boxes demand that each coronary artery be treated as a distinct object, reducing the risk of blending different structures into a single segmented region.