## 4 Discussion  
Based on Table 2 (first part) and Figure 4, the 3D U-net approach was the most satisfactory approach for automatic segmentation of bladder and rectum on CBCT scans. This supported the initial hypothesis that registration-based approaches failed in the case of large deformation and alternative approaches using the statistics of the target image (i.e., the CBCT scan) were more suitable. This observation was also consistent with the state-of-the-art algorithms shown in Table 2 (second part), where DL approaches outperformed alternative approaches for bladder and rectum. Still based on Table 2 (first part) and Figure 4, the 3D U-net slightly outperformed the registration-based approaches for prostate, but this improvement was not statistically significant. 3D U-net's lower performances for prostate than for bladder and rectum was further supported by the Bland-Altman analysis of the manual and predicted volumes. Indeed, this analysis provided less than 5% bias for all organs, but higher precision (i.e., a larger spread of the predictions, as defined in (5)) for prostate than for bladder and rectum. Furthermore, most other state-of-the-art DIR-based algorithms outperformed our approach for prostate. This showed that DIR-based approaches were still valuable in situations with limited organ deformation and where poor contrast made the use of vanilla DL models challenging. A first way to improve the segmentation results for prostate and outperform DIR-based approaches without annotating more CBCT scans might be to generate pseudo CBCT scans as in [PERSON] et al., but our study showed that increasing the number of already annotated CT scans further was a valuable alternative, albeit with a risk of saturation. If few data are available,  
Figure 6: Bland–Altman plots for bladder, rectum, and prostate derived from the differences between the predicted and manual segmentations. The solid lines represent no difference; the dotted lines depict the mean difference (bias) and 95% limits of agreements (LoA). a second option could be to promote a desired shape or structure in the deep model prediction [32; 33]. A third option could be to perform unsupervised domain adaptation [34]. This approach requires annotations in a source domain (CT), but not in the target domain (CBCT). This will be the subject of future research. From an application point of view, the study showed that the more CBCT scans were contoured, the better the DSC on the predicted contours. However, contouring CBCT scans is not part of the clinical workflow, is time consuming, and is not easy because of the poor contrast between the different regions of interest. Hence, we showed that expanding the training set with CT scans improved the segmentation performances for all considered organs, especially when few contoured CBCT scans were available. Our 3D U-net that reached the best segmentation performances was trained with 42 CBCT and 74 CT scans. Most cases of failure were observed for prostate, which had the lowest DSC of the organs. This may be due to the fact that prostate is hard to see on CBCT scans and often pushes on bladder, as we can see in Figure 5. Hence, some upper parts of prostate were often wrongly classified as bladder, which decreased the DSC for prostate. Since bladder is larger than prostate, misclassification at the boundary between the two organs had less impact on the DSC of bladder. A second case of failure occurred at the top and bottom slices of rectum, which was wrongly classified as the background (or inversely, the background was wrongly classified as rectum). This made sense since there were few differences in contrast between rectum, anal canal, and colon. The impact of such errors on prostate and rectum, as well as the required contour quality for clinical use in adaptive radiotherapy, was such that additional quality assessment with a contours review process was needed. This should be done by radiation oncologists and will be the subject of future research.