## 4 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time (s)** & **Atlas-Based Auto-Contouring (Mean (CI))** & **Deep Learning Auto-Contouring (Mean (CI))** & **\(p\)-Value** \\ \hline Contouring & 153.43 (150.63, 156.08) & 69.53 (68.40, 70.58) & \textless{}0.001 \\ Review and Editing & 544.55 (495.02, 612.48) & 352.98 (315.40, 397.92) & \textless{}0.001 \\ Total & 697.97 (647.55, 764.92) & 422.51 (385.39, 467.14) & \textless{}0.001 \\ \hline \hline \multicolumn{4}{c}{CI-95\% confidence interval.} \\ \end{tabular}
\end{table} 
Table 3: Comparison of time required for atlas-based and deep learning auto-contouring. nerves when compared with their pre-trained DL model. Although this study shows that the RSL Head and Neck CT 2.0.0.47 DL auto-contouring tool had higher mean DSC values for contouring the left and right optic nerves than its counterpart, aligning with [PERSON] et al. 's findings, its performance in contouring two other small structures, left and right cochleae was the worst (mean DSC values: 0.31-0.35). Notable discrepancies between mean DSC and HD/HD95 values of these two OARs (mean HD95 of left and right cochleae: 3.92 mm (good) and 4.04 mm (intermediate), respectively) were also found. Despite that the DSC and HD/HD95 are parameters to evaluate the geometric accuracy of contours, the DSC is the indicator of shape similarity while HD/HD95 are the parameters to illustrate location similarity. HD95 is generally considered a better descriptor of location similarity than the HD due to its capability of excluding the outliers [6, 27]. Similar discrepancies between the DSC and HD values were also found in [PERSON] et al. 's [5] study. For example, their pre-built DL model had the mean DSC values of about 0.8 (good) for oral cavity and left and right parotid glands but the corresponding mean HD values were more than 17 mm (poor). In order to address these discrepancies and improve the geometric accuracy of the DL auto-contouring tool, [PERSON] et al. [5] used 120 datasets from their local hospital database to finetune the DL model. In this way, the DL model could learn the local contouring protocols and become more capable to accurately contour the OARs as per the hospital's requirements. After further training, their AccuContour DL model was able to achieve DSC values of all 14 OARs above 0.7. This highlights finetuning of the commercial DL model is key to improve its geometric accuracy. In this study, discrepancies between the local hospital contouring protocol and DL auto-contouring tool were also found. For example, Figure 4 shows the notable difference of the spinal cord caudal limits of manual contouring protocol and the DL approach. It is expected that further training of the RSL Head and Neck CT 2.0.0.47 DL model would help to reduce these discrepancies and improve its accuracy [29]. This study had several major limitations. Only 45 nasopharyngeal cancer datasets were used for the atlas-based and DL auto-contouring tool evaluation. However, the number of evaluation datasets of this study was more than a double of the dataset numbers of the similar studies by [PERSON] et al. [5] (20 datasets) and [PERSON] et al. [6] (22 datasets). Additionally, this study covered 16 OARs which was greater than the numbers of OARs (4-14) of other similar studies [5, 6, 10]. Although only one radiation therapist with more than 10 years of experience in head and neck RT planning was involved in the contouring time evaluation, the international consensus delineation guidelines were used for review and editing the contours for minimizing the inter- and intra-operator variabilities [5, 28]. Besides, this study only evaluated one DL auto-contouring model and did not investigate dosimetric impact of contouring accuracy but these arrangements were consistent with the other similar studies [5, 6, 10, 27].