## 5 Conclusions  
In this study, we demonstrated that a bounding box-based object detection framework can effectively identify and localize the three major coronary arteries (RCA, LCA-LAD, and LCA-CX) in cross-sectional CCTA images. By enriching our evaluation beyond the conventional Average Precision (AP) metric to include IoU Above Threshold Rates, DSC Above Threshold Rates, and MAE, we obtained a more thorough understanding of the method's spatial precision and positional alignment. Although the LCA-LAD exhibited generally superior performance across most metrics, class-dependent differences highlighted the variability in coronary anatomy and imaging conditions that can challenge machine learning-based detection.  
These findings underscore the merit of bounding box detection in reducing the risk of misidentifying cardiac veins or other structures as arterial cross-sections. Future work will include validating the framework on larger, multi-institutional datasets, refining artifact correction strategies, and integrating bounding box detections with 3D visualization tools. Ultimately, these efforts aim to further streamline and standardize CCTA interpretation, improving diagnostic workflows and patient outcomes in ischemic heart disease management.  
[PERSON] contributed to the data analysis, algorithm construction, and writing and editing of the manuscript. [PERSON] reviewed and edited the manuscript. [PERSON] proposed the idea and contributed to the data acquisition, performed supervision and project administration, and reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study was conducted by the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of Hokkaido University Hospital (No. 016-0495: May 2017).  
This study is a retrospective study; therefore, the information has already been disclosed on the website of the institution by the opt-out method.  
Data Availability Statement:Due to privacy and ethical restrictions, the dataset is not publicly available. The models created in this study are available on request from the corresponding author. The source code of this study is available at [[https://github.com/MIA-laboratory/CCTA_artery_detection/](https://github.com/MIA-laboratory/CCTA_artery_detection/)]([https://github.com/MIA-laboratory/CCTA_artery_detection/](https://github.com/MIA-laboratory/CCTA_artery_detection/)) (accessed on 8 February 2025).  
The authors would like to thank the laboratory members of the Medical Image Analysis Laboratory for their help.  
The authors declare that no conflicts of interest exist.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IHD & Ischemic Heart Disease \\ CAD & Coronary Artery Disease \\ CCTA & Coronary Computed Tomography Angiography \\ MIP & Maximum Intensity Projection \\ CPR & Curved Planar Reformation \\ CNN & Convolutional Neural Network \\ ECG & Electrocardiogram \\ ROI & Region of Interest \\ RCA & Right Coronary Artery \\ LCA-LAD & Left Coronary Artery-Left Anterior Descending \\ LCA-CX & Left Coronary Artery-Circumflex \\ LMT & Left Main Trunk \\ IoU & Intersection over Union \\ DSC & Dice Similarity Coefficient \\ MAE & Mean Absolute Error \\ AP & Average Precision \\ SGDM & Stochastic Gradient Descent with Momentum \\ CPR & Curved Planar Reformations \\ CT & Computed Tomography \\ MRI & Magnetic Resonance Imaging \\ X-ray & X-ray Imaging \\ \end{tabular}