## 4 Discussion  
NifteRT on linac-MR demands adequate tumor contouring accuracy, as well as contouring speed, which cannot be achieved by humans' manual contouring. While automatization of tumor delineation using a deep neural network may provide a solution for this, finding the optimal set of hyperparameters of the network that results in satisfactory contouring performance remains a challenging problem. In this work, CMA-ES was implemented for patient-specific HPO within a U-Net-based autocontouring algorithm. This study clearly demonstrates that the developed autocontouring routine is able to perform HPO to generate accurate autocontours for various patients that are comparable to experts' manual contours. With the limited search ranges used in this study, the HP-optimized U-Net architecture for each patient was simpler than that of the original U-Net, as shown in Figure 4. In Figure 4, the architecture was reduced by using one 5 \(\times\) 5 convolution, 32 feature maps, and one pooling. The ability to autocontour with a simpler network architecture is beneficial as it leads to faster training and autoconturing with less demanding computing power. Overall, some general trends in the optimized hyperparameters were observed. For the number of convolutions, using one convolution was preferred for lung patients. For filter size, a strong preference for using 5 \(\times\) 5 was observed for all patients. For learning rate, a relatively lower learning rate was preferred for prostate patients (e.g., median of 1.72 \(\times\) 10\({}^{-4}\) for prostate and 4.84 \(\times\) 10\({}^{-4}\) for lung). No trends were observed for the remaining hyperparameters. For the input range of the number of training images, annotating 30 images instead of 10 may not seem to dramatically increase the workload of a clinician. However, considering the total number of images that had to be annotated (i.e., 6110 images for 47 patients), this is a considerable increase in workload. Thus, reducing the number of training images as much as possible was the goal of its optimization. Also, the evaluation of autocontours revealed the overall promising contouring performance of the algorithm, which achieved a mean DC of \(\geq\)0.9 for all the studied patients except four liver patients (P8-11), two prostate patients (P21 and 22), and two lung patients (P46 and 47). P10 and 11 had relatively lower mean DCs of 0.86 and 0.85, respectively, which can be largely attributed to the fact that the tumors were hardly visible on the images. P22 also had a relatively lower mean DC of 0.86, which may be due to the pronounced artifacts on the images. The autocontouring performance was generally worse for liver patients than that for prostate patients because of the lower tumor contrast with the normal liver tissue background. For prostate patients, the sagittal (P12-22) and coronal cases (P23-27) were more challenging to contour than the axial cases (P28-35) due to the diffuse boundary of the prostate, and therefore, this mostly resulted in lower DC and higher CD and HD than those for the axial cases. Moreover, the algorithm was able to \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**\# of Patients Used for HPO**} & \multicolumn{2}{c}{**DC**} & \multicolumn{2}{c}{**CD (mm)**} & \multicolumn{2}{c}{**HD (mm)**} \\ \cline{2-7}  & **Mean/SD** & **Max/Min** & **Mean/SD** & **Max/Min** & **Mean/SD** & **Max/Min** \\ \hline (i) Patient-specific HPO & 0.92/0.04 & 0.97/0.83 & 1.35/1.03 & 3.95/0.15 & 3.63/2.17 & 7.51/2.01 \\ (ii) 30 patients (1 training and 1 validation image per patient) & 0.64/0.05 & 0.77/0.53 & 7.34/2.35 & 13.28/3.14 & 15.81/3.53 & 24.31/9.95 \\ (iii) 9 patients (30 training and 30 validation images per patient) & 0.36/0.09 & 0.57/0.17 & 9.44/3.46 & 18.49/2.66 & 19.40/3.81 & 29.64/11.48 \\ (iv) 15 patients (30 training and 30 validation images per patient) & 0.47/0.07 & 0.63/0.33 & 8.54/3.32 & 16.03/3.29 & 17.03/4.09 & 25.96/9.59 \\ \hline \hline \end{tabular}
\end{table}