## 2 Materials and Methods
### Model Architecture and Learning Strategy  
Bladder, rectum, and prostate were segmented on CBCT scans using the 3D U-net fully convolutional neural network [21, 26]. The 3D input went through a contracting path to capture context and an expanding path to enable precise localization. In the last layer, a softmax was applied, and the network outputs the probability of each voxel belonging to bladder, rectum, prostate, or none of these organs. The network architecture is shown in Figure 3. To obtain a binary mask for each organ, the most probable class label was assigned to each pixel individually. In practice, each organ was segmented as a single region of connected voxels. No disconnected region of the same organ was observed. The main advantage of fully convolutional neural networks is that they output predictions at the same resolution as the input. One output channel was considered per organ. The network was trained with the Dice loss. The Adam optimization algorithm was used with a learning rate of \(10^{-4}\). The number of epochs was chosen such that convergence was reached. The hyper-parameters mentioned here were the same as in [PERSON] et al. [24] and proved satisfactory on the data used in this work. For this reason and to keep data available for training and testing, no validation set was considered here. Training data were augmented online using rotation (between \(-5^{\circ}\) and \(5^{\circ}\) along each of the three axes), shift (between \(-5\) and \(5\) pixels along each axes), and shear (reasonable values for the affine transformation matrix). The batch size was set to two, which was the maximum size affordable on our 11 Gb graphical processing units (GPU).  
We performed 3-fold cross-validation with the 63 CBCT scans of set \(\mathcal{S}_{2}\), where 2 folds (\(n_{CBCT}\leq 42\) volumes in total) were used as the training set and one fold (21 volumes) as the test set, as shown in Table 1. The number of training CBCT scans \(n_{CBCT}\) was varied such that \(n_{CBCT}\in\{0,6,10,20,30,42\}\). The training set was augmented with \(n_{CT}\) annotated CT scans from set \(\mathcal{S}_{1}\) such that \(n_{CT}\in\{0,20,74\}\). The same CT scans were added to the training CBCT scans independently of the considered training folds. Hence, the training set contained \(n_{CBCT}+n_{CT}\) volumes in total. Note that the test set contained no CT scans (since our goal was to segment CBCT scans only). The source code is publicly available at [[https://github.com/eliottbrion/pelvis_segmentation](https://github.com/eliottbrion/pelvis_segmentation)]([https://github.com/eliottbrion/pelvis_segmentation](https://github.com/eliottbrion/pelvis_segmentation)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\(\mathcal{S}_{1}\) (CT)} & \multicolumn{3}{c}{\(\mathcal{S}_{2}\) (CBCT)} \\ \cline{2-4}  & **fold 1** & **fold 2** & **fold 3** \\ \hline train & train & train & _test_ \\ train & train & _test_ & train \\ train & _test_ & train & train \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three-fold cross-validation. To train the model, we used \(n_{CT}\) CT scans from \(\mathcal{S}_{1}\) and the \(n_{CBCT}\) first volumes from the CBCT folds labeled “train.” To test the model, we used all 21 volumes from the CBCT fold labeled “test.”  
Figure 2: Case selection from CHU-Charleroi Höpital André Vesale and CHU-UCL-Namur.