## 3 Results
For the methods with worse performance, contours varied considerably between images in the 70-testing-image set for some patients (e.g., P8 and 19). Thus, contours that deviated considerably from the manual contours and that were more representative of other contours in the set are shown in Figure 7. P8, 20, and 47 were the cases for which nnU-Net showed worse performance in terms of DC, CD, or HD. For P20 and 47, the small islands close to the right boundary were not removed by nnU-Net, resulting in very large HD values. P11 was the case in which all three methods did not perform well (lowest mean DC of all liver patients), P19 was the case in which non-optimized U-Net showed the worst performance, and P46 was the case in which both non-optimized U-Net and nnU-Net showed the worst performance. The mean (SD) autocontouring time per image of the 47 patients was found to be 54.2 (0.3) ms for HP-optimized U-Net, 55.6 (0.3) ms for non-optimized U-Net, and 4855 (610) ms for nnU-Net. Figure 6: (**a**) Dice’s coefficient, (**b**) centroid displacement, and (**c**) Hausdorff distance between manual contours and autocontours generated by different autocontouring methods for liver (P1–11), prostate (sagittal: P12–22, coronal: P23–27, axial: P28–35), and lung (P36–47) cancer patients. In Table 3, the autocontouring performance of the proposed algorithm using patient-specific HPO is compared with that of non-patient-specific HPO using four different scenarios: (i) patient-specific HPO as explained in Section 2.3; (ii)-(iv) non-patient-specific HPO; (ii) data of 30 randomly selected patients used for training (1 training and 1 validation image per patient); (iii) data of 9 randomly selected patients used for training, 3 patients per tumor site (30 training and 30 validation images per patient); and (iv) data of 15 randomly selected patients used for training, 5 patients per tumor site (30 training and 30 validation images per patient). In each scenario, testing was performed on 70 testing images for each of the 47 patients. The evaluation metrics comparing the generated autocontours against \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{5}{c}{**Comparing HP-Optimized U-Net with**} & \multicolumn{3}{c}{**Comparing HP-Optimized U-Net with nnU-Net**} \\ \cline{2-7}  & **\# of Patients with** & **\# of Patients with** & **\(p\) < 0.05** & **\# of Patients with** & **\# of Patients with** & **\(p\) < 0.05** \\ \cline{2-7}  & **\# of Patients with** & **\# of Patients with** & **\# of Patients with** & **\# of Patients with** & **\# \(p\) < 0.05** \\ \cline{2-7}  & **\#** & **\# Two-Tailed** & **One-Tailed** & **Better Mean Value** & **Two-Tailed** & **One-Tailed** \\ \hline DC & 36 & 33 & 32 & 37 & 31 & 30 \\ CD & 32 & 25 & 21 & 27 & 26 & 19 \\ HD & 32 & 26 & 24 & 39 & 37 & 33 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of patient cases (out of 47 patients) that showed statistical significance based on the paired \(t\)-test comparing HP-optimized U-Net with the standardized methods across the evaluation metrics. A \(p\)-value smaller than 0.05 was considered to represent statistical significance. Figure 7: Comparison of example contours (magnified for better visibility) generated by different autocontouring methods for six cancer patients. the manual contours in each scenario are shown in this table. Patient-specific HPO achieved the best overall contouring performance.