## 3 Results
However, we observed that the difference in the average DSC between Ours(20, 0) and Ours(20, 20) was approximately equal to the difference the in average DSC between Ours(20, 20) and Ours(20, 74), whereas 20 new CT scans were added to the training set in the first case and 54 new CT scans in the second case. This may indicate saturation of the performance improvement produced by adding CT scans to the training set. Moreover, when the number of training CBCT scans was large, adding training CT scans improved performance for rectum only (\(p<10^{-2}\)): no statistically significant incremental change in performance was observed for bladder or prostate (\(p=0.780\) and \(p=0.630\), respectively) when Ours(42, 74) and Ours(42, 0) were compared. A plausible interpretation was that most of the useful information present in the CT scans was already captured in the relatively large CBCT training set. More importantly, in line with our objective of limiting the annotation of CBCT scans, we observed that the performance obtained with 42 CBCT and 0 CT scans could be reached with 20 CBCT and 74 CT scans for all organs (bladder \(p=0.940\), rectum \(p=0.882\), prostate \(p=0.994\)). Hence, the availability of 74 annotated CT scans reduced the number of annotated CBCT scans significantly (by a factor of approximately two). Third, when all available CT and CBCT scans (42 CBCT and 74 CT scans) were used for training, our approach significantly outperformed the rigid registration, RayStation DIR algorithm, and diffeomorphic morphons' algorithm for bladder and rectum (\(p<10^{-3}\)), but not for prostate (\(p=0.911\)). These conclusions are illustrated on a representative patient in Figure 5. The results also showed that the rigid registration was outperformed by the ANACONDA algorithm, which was in turn outperformed by the diffeomorphic morphons' algorithm for bladder and rectum. As mentioned above, both DIR methods were statistically similar to the rigid registration approach when it came to segmenting prostate. This supported the hypothesis that prostate underwent less deformation than bladder and rectum, which were subject to regular influxes and voiding of matter. Although our analysis was based on the DSC, both JI and SMBD led to the same conclusions. Figure 6 presents Bland-Altman plots comparing the organ volumes reached manually and by our DL-based predictions (obtained with Ours(42, 74)), using the bias, precision, and 95% limits of agreements (LoA). The bias normalized by the manual volume was below 5 % for all organs (bladder 4.78%, rectum 1.21%, prostate 2.51%). The precision normalized by the manual volume was similar for bladder and rectum (bladder 13.3%, rectum 13.9%) and larger for prostate (27.9%). The LoA of bladder were also close to the LoA of rectum (\(-32\%\) and 41% for bladder and \(-33\%\) and 35% for rectum), whereas they were larger for prostate (\(-65\%\) and 70%). Table 3 completes the Bland-Altman plots by providing the means and standard deviations for the manual and predicted organ volumes. Moreover, a one-sample \(t\)-test was performed on the differences between the manual and predicted volumes normalized by the manual volume for each organ. The resulting \(p\)-values for all organs are presented in Table 3 and were not significantly different (bladder \(p=0.285\), rectum \(p=0.897\), prostate \(p=0.438\)). This meant that the predicted and manual contours were similar in means according to the \(t\)-test. Computational cost analysis was performed by measuring the running time on our machine equipped with an 11 Gb GeForce GTX 1080 Ti graphics card. The rigid registration of one image ran in 1.05 min. The deformable image registration with the ANACONDA and morphons' algorithms ran in 1.92 min and 8.33 min, respectively. The inference time for one image with the DL approaches was much lower. It reached 0.15 s independently of the learning strategy. Indeed, the number of images in