## 3 Results  
In this section, we assess the performance of our algorithm in terms of overlap (i.e., DSC and JI), distance (i.e., SMBD), and volume agreement measurements. In the first part, we compare the overlaps and distances measured between our algorithm in different settings and the considered DIR-based segmentation approaches. In the second part, we further evaluate the performance of our best algorithm (i.e., 3D U-net trained with all available CT and CBCT scans) by assessing whether the predicted organ volumes are in good agreement with the volumes determined by manual segmentation. This was done by Bland-Altman analysis. In Figure 4, the DSCs between the segmentation output of the fully convolutional neural network (FCN) and the ground truth segmentation were computed and averaged over all 63 CBCT scans from the three test folds. This was done for different numbers of training CBCT and CT scans. The results were then compared with the RayStation DIR algorithm, the diffeomorphic morphons' algorithm, and rigid registration. Table 2 completes the plots in Figure 4 by providing the means and standard deviations of DSC, JI, and SMBD for different numbers of training CBCT scans and different numbers of training CT scans. The statistical model used for comparing the performances was a mixed model with a random intercept on the patient. It showed significant differences between algorithms' performance for all organs regarding their DSC (bladder, rectum, prostate \(p<10^{-3}\)), JI (bladder, rectum, prostate \(p<10^{-3}\)), and SMBD (bladder, rectum, prostate \(p<10^{-3}\)). In the following paragraphs, the notation \(\text{Ours}(n_{CBCT},n_{CT})\) stands for the 3D U-net proposed in this study with \(n_{CBCT}\) CBCT scans and \(n_{CT}\) CT scans in the training set. The \(p\)-values provided below were obtained by performing a [PERSON]'s range test on the DSCs. The following observations can be made based on Figure 4 and Table 2.  
First, CBCT scans were more valuable than CT scans to train a CBCT segmentation model. This was not surprising and supported by the observation that a model trained on 40 CBCT and 0 CT scans performed significantly better than a model trained on 0 CBCT and 40 CT scans for all organs (bladder, rectum, prostate \(p<10^{-3}\)). The DSCs reached 0.634, 0.286, and 0.525 with Ours(0, 40) and 0.845, 0.754, and 0.722 with Ours(40, 0), for bladder, rectum, and prostate, respectively. Furthermore, a model trained only on 74 CT scans reached approximately the same performance as a network trained on only six to 10 CBCT scans for all the organs. Moreover, the more CBCT scans there were in the training set, the higher the DSCs on the test set were. This result made sense since adding new CBCT scans to the training set allowed the network to generalize on the test set (exclusively composed of CBCT scans) better. More surprisingly, we observed that once a sufficient number (typically 20) of CBCT scans were part of the training set, the benefit of adding CBCT or CT scans was practically the same. Indeed, compared with a model trained on 20 CBCT and 20 CT scans, the model trained on 40 CBCT  
Figure 4: Influence of the number of training CBCT and CT scans on the DSC. Bars indicate one standard deviation for the group of 63 patients. DSC: Dice similarity coefficient. and 0 CT scans did not lead to a significant improvement in performance (bladder \(p=0.877\), rectum \(p=0.700\), prostate \(p=0.629\)). The DSCs reached 0.815, 0.731, and 0.682 with Ours(20, 20) for bladder, rectum, and prostate, respectively. This confirmed that augmenting a CBCT training set with CT scans might be quite valuable. Second, from the CT perspective, we clearly observed that the more CT scans there were in the training set, the higher the mean DSC became. Indeed, Ours(20, 74) was significantly better than Ours(20, 0) for all organs (bladder, rectum \(p<10^{-3}\), prostate \(p<10^{-2}\)). We explained this improvement by the learning of more generic features, leading to better generalization.