## 4 Discussion  
In this paper, a robust and generalizable model for accurate prostate segmentation has been proposed. To achieve robustness, the model was trained with five very challenging and heterogeneous MR prostate datasets (and two US prostate datasets) with GTs originating from many different experts with varying segmentation criteria. Additionally, several key design choices, such as the use of Checkpoint Ensembling and a relatively heavy data augmentation regime, were explicitly made. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment Description**} & \multicolumn{3}{c}{_DSC_} & \multicolumn{3}{c}{**HD95 (mm)**} & \multicolumn{3}{c}{**ABD (mm)**} \\ \cline{2-10}  & **Mean** & **Median** & **Min.** & **Mean** & **Median** & **Max.** & **Mean** & **Median** & **Max.** \\ \hline Baseline (4M params.) & 0.9150 & 0.9179 & 0.8094 & 3.8693 & 3.9995 & 7.2000 & 0.9815 & 0.9311 & 2.0356 \\ \hline No neural Resolution & & & & & & & & & \\ Enhancement & & & & & & & & & \\ No Checkpoint & & & & & & & & & \\ Ensembling & & & & & & & & & \\ \hline \multirow{2}{*}{MR} & One less resolution & & & & & & & & & \\ reduction level & & & & & & & & & \\ Half the amount of layers & & & & & & & & & \\ per level & & & & & & & & & \\ \multicolumn{1}{c}{} & Resnet with 20 layers (7M & & & & & & & \\ params.) & & & & & & & & & & \\ \multicolumn{1}{c}{} & Resnet with 72 layers & & & & & & & & \\ \multicolumn{1}{c}{} & (16M params.) & & & & & & & & \\ ReLU instead of PReLU & & & & & & & & & \\ activations & & & & & & & & & \\ No Deeep Supervision & & & & & & & & & \\ \hline \multirow{2}{*}{US} & Baseline & & & & & & & & & \\ No transfer learning & & & & & & & & & \\ (fine-tuning) & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation studies. In clinical practice, the MR and US images may originate from many different scanners, with widely different characteristics (field intensities, scanner manufacturers, use of endorectal coil, etc. ), to which any segmentation algorithm should be robust by design. As we have seen in Table 2, the proposed model has a similarly good performance for all images, no matter the dataset nor its specific characteristics. Furthermore, such an algorithm may be used by different experts with varying criteria for segmenting the prostate. Even if it is impossible to please every criterion simultaneously, the proposed model is shown to behave as an average prediction among the different experts, as seen for instance in the rightmost slice of Figure 6j. This is corroborated by Table 3, where it shows a significantly higher overlap with any given expert (it tries to please all criteria), than the experts between themselves. Concerning generalizability, the proposed architecture can be very easily applied to different tasks by means of transfer learning. In this paper, the MR segmentation model is simply retrained, with no hyperparameter tuning or image pre- or post-processing changes (other than the change of input resolution), on the problem of US prostate segmentation, achieving equally good performances despite the smaller dataset sizes, as seen in Table 2.  
The main clinical applications of the proposed model lie in the context of fusion-guided biopsy and focal therapies on the prostate, which require an accurate segmentation of both MR and US prostate images. These segmentations are employed to perform registration between both modalities, which is needed to transfer the prostate lesions detected by the radiologists in the preoperative MR to the intraoperative US image in order to guide the procedure. The proposed model can undercut, and even eliminate, the need of manual segmentations, which require expertise, are very time-consuming, and are prone to high inter- and intra-expert variability.