## 4 Discussion  
To the best of our knowledge, this was the first study on comparing the performances between RaySearch Laboratories AB RayStation DL auto-contouring model, RSL Head and Neck CT 2.0.0.47 and its atlas-based auto-contouring software, ANACONDA for the OARs segmentation in the head and neck RT. This study's results show that the RSL Head and Neck CT 2.0.0.47 DL auto-contouring approach could achieve more consistent performance in OARs segmentation than the ANACONDA atlas-based approach (Figure 2). These findings are in line with similar studies on comparing the INT Contour DL auto-contouring approach with the Maestro 6.9.6 and ANACONDA atlas-based approaches for head and neck RT [6,10]. This consistent performance resulted in less time required for manual correction because the required adjustments became more straightforward and could be completed rapidly. Hence, this contributed to the statistically significant reduction of the review and editing time and the time required for the whole segmentation process by 35% and 40%, respectively (Table 3). For [PERSON] et al. 's [6] study, the INTContour DL and Maestro 6.9.6 atlas-based approaches required 120 s and 600 s to contour 4 head and neck OARs, respectively. Although this represented that their DL approach was able to reduce the total contouring time by 80%, the number of OARs involved in their study was only one-fourth of the number in this study. Hence, the total contouring time required for the RSL Head and Neck CT 2.0.0.47 DL auto-contouring approach appears comparable to their study. These findings are particularly important for addressing the major issues of the manual contouring, namely inter- and intra-operator variabilities and labor-intensiveness in head and neck RT [1, 2, 3, 4, 5]. To further address the manual contouring issue, high geometric accuracy of contours is essential. This study's results show that the RSL Head and Neck CT 2.0.0.47 DL auto-contouring had statistically significantly higher mean DSC and lower HD and HD95 values for 10 out of 16 OARs, namely brain stem, left and right eyes, left and right lens, left and right optic nerves, left and right parotid glands, and pituitary when compared with those of the ANACONDA atlas-based approach (Table 2). However, the highest mean DSC value achieved by the RSL Head and Neck CT 2.0.0.47 DL auto-contouring approach was only 0.88 for the right eye and the lowest one was 0.31 for the right cochlea. For [PERSON] et al. 's [5] study on comparing the performances of Maestro 6.6.5 atlas-based, pre-trained and trained AccuContour DL auto-contouring tools for head and neck OARs contouring, the highest mean DSC value achieved by their pre-trained DL tool was above 0.9 but four (left and right temporomandibular joints and left and right optic nerves) out of 14 OARs contoured by their pre-trained DL model had mean DSC below 0.6. In contrast, their Maestro 6.6.5 atlas-based auto-contouring tool performed better for these four OARs. In this study, five (left and right cochleae, larynx, optic chiasm and spinal cord) out of 16 OARs contoured by the pre-trained RSL Head and Neck CT 2.0.0.47 DL model had mean DSC below 0.6. Except larynx, the ANACONDA atlas-based approach had higher mean DSC values for these OARs. The DSC findings of this study appears comparable to the corresponding tools in [PERSON] et al. 's study [5]. According to [PERSON] et al. 's [27] study on comparing the WorkflowBox 1.4 atlas-based auto-contouring tool with the Mirada Medical Limited DLCE Expert DL auto-contouring model for head and neck RT, the DL model was more accurate to contour small OARs. However, this was not supported by [PERSON] et al. 's [5] findings. For example, their Maestro 6.6.5 atlas-based auto-contouring tool was more accurate to contour the left and right optic