## 3 Results
### Evaluation of Detection Accuracy Using Additional Metrics
#### 3.2.3 Mean Absolute Error (MAE)  
The localization accuracy was evaluated using the MAE, which measured the average deviation between the predicted and ground truth bounding box centers. The results are presented in Table 4. The mean MAE values were 15.6 mm for the RCA, 10.5 mm for tyeh LCA-LAD, and 5.8 mm for the LCA-CX. The LCA-CX achieved the best localization precision, reflected in the lowest MAE value. Conversely, the RCA had the highest MAE, indicating larger deviations in localization. Fold-wise analysis revealed that the LCA-CX consistently maintained low MAE values across all folds, with the best performance of 4.5 mm in fold5. Meanwhile, RCA exhibited the highest MAE of 18.3 mm in fold4, showing variability in its localization accuracy.  
Overall, the results indicate that the object detection model demonstrated the best overlap-based performance (IoU, DSC) in detecting and localizing the LCA-LAD, followed by the RCA, whereas the LCA-CX exhibited lower overlap metrics but the best localization accuracy, as evidenced by the smallest MAE. These findings underscore that different evaluation metrics can highlight different aspects of detection accuracy, and they collectively provide a comprehensive assessment of the model's performance across multiple arterial classes.  
Figure 3 illustrates an example of this variability. The yellow arrow highlights a coronary artery that appears relatively faint, leading to a missed detection by the model despite being annotated as a ground truth. In contrast, the red arrow indicates a calcified lesion with a high CT value, which was accurately detected at the vessel center. This outcome suggests that the model may not be heavily influenced by random high-intensity values such as calcification and instead appears more sensitive to low-contrast vessels. Coronary artery detection can fail when the arterial lumen appears only slightly brighter than the surrounding tissues, whereas strong calcifications might not necessarily degrade localization performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**MAE [mm]**} \\ \hline  & **RCA** & **LCA-LAD** & **LCA-CX** \\ \hline fold1 & 13.1 & 10.1 & 4.6 \\ fold2 & 15.7 & 13.4 & 5.4 \\ fold3 & 13.9 & 9.4 & 7.9 \\ fold4 & 18.3 & 12.2 & 6.8 \\ fold5 & 17.2 & 7.5 & 4.5 \\ \hline mean & 15.6 & 10.5 & 5.8 \\ \hline \hline \end{tabular}  
* RCA = Right Coronary Artery; LCA-LAD = Left Coronary Artery–Left Anterior Descending; LCA-CX = Left Coronary Artery–Circumflex; MAE = Mean Absolute Error.  
\end{table}
Table 4: Mean Absolute Error (MAE) for RCA, LCA-LAD, and LCA-CX Across Five Folds.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RCA** & **LCA-LAD** & **LCA-CX** \\ \hline fold1 & 79.8 & 78.8 & 80.4 \\ fold2 & 77.8 & 80.0 & 76.6 \\ fold3 & 84.0 & 85.0 & 77.0 \\ fold4 & 80.5 & 85.5 & 66.1 \\ fold5 & 73.1 & 76.0 & 68.9 \\ \hline mean & 79.0 & 81.1 & 73.8 \\ \hline \hline \end{tabular}  
* RCA = Right Coronary Artery; LCA-LAD = Left Coronary Artery–Left Anterior Descending; LCA-CX = Left Coronary Artery–Circumflex; DSC = Dice Similarity Coefficient.  
\end{table}
Table 3: DSC Above Threshold Rates for RCA, LCA-LAD, and LCA-CX Across Five Folds.