# Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Organ** & **Evaluation Metrics** & **Full-Scratch** & **ImageNet** & **RadImageNet** \\ \hline \multirow{6}{*}{Kidney} & \multirow{6}{*}{Dice [\%]} & Mean & 94.1 & 93.9 & 93.1 \\  & & SD & 3.6 & 4.1 & 4.1 \\  & & Median & 95.2 & 95.2 & 94.5 \\  & & Min & 72.1 & 71.1 & 72.1 \\  & & Max & 97.3 & 97.1 & 97.1 \\  & & Sensitivity [\%] & 97.7 & 98.0 & 97.9 \\  & & False positive rate [\%] & 9.1 & 9.6 & 10.9 \\ \hline \multirow{6}{*}{Spleen} & \multirow{6}{*}{Dice [\%]} & Mean & 90.9 & 91.3 & 90.3 \\  & & SD & 8.3 & 8.2 & 7.9 \\  & & Median & 92.6 & 92.6 & 92.0 \\  & & Min & 20.7 & 20.6 & 24.1 \\  & & Max & 96.4 & 96.3 & 95.8 \\  & & Sensitivity [\%] & 95.4 & 95.8 & 95.6 \\  & & False positive rate [\%] & 12.9 & 12.6 & 14.2 \\ \hline \multirow{6}{*}{Pancreas} & \multirow{6}{*}{Dice [\%]} & Mean & 71.9 & 75.1 & 70.6 \\  & & SD & 14.0 & 12.5 & 15.8 \\ \cline{1-1}  & & Median & 75.9 & 78.9 & 75.2 \\ \cline{1-1}  & & Min & 21.7 & 25.1 & 3.6 \\ \cline{1-1}  & & Max & 88.4 & 89.4 & 86.4 \\ \cline{1-1}  & & Sensitivity [\%] & 75.7 & 82.3 & 77.3 \\ \cline{1-1}  & & False positive rate [\%] & 29.2 & 29.3 & 33.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._However, the accuracy of organ extraction is lower in patients with smaller body sizes. Figure 5 shows an example of the extraction results for the case with small body size. The reason for the low extraction accuracy of the pancreas is thought to be the unclear margins of the organ, which is a feature of LDCT images. In addition, because the images were captured with a wide FOV, the organ regions in the images were small, and the features of the pancreas were likely to be lost. For the spleen, only the extraction accuracy of the \(\text{Candidate}_{\text{PET/CT}}\) was low. The axial image in Figure 5 shows the edge of the spleen, which was not correctly extracted because of low FDG uptake. Furthermore, this study used fewer datasets than the existing studies. Therefore, we compared the extraction accuracy using pre-trained datasets to improve the learning stability and increase the generalization ability. As shown in Table 2, when evaluated using the Dice index, there was no significant difference between full-scratch and ImageNet for the liver, kidney, and spleen; moreover, the RadImageNet was lower than that of the other two models. For the pancreas, ImageNet was 3.2% and 4.5% more accurate than full-scratch and RadImageNet, respectively; furthermore, ImageNet showed only 3.2% and 4.5% higher Dice indices than did full-scratch and RadImageNet, respectively. This is because ImageNet and RadImageNet were better at improving sensitivity than full-scratch for all organs, and were able to extract more regions of the organs. However, the FP rate increased significantly only for RadImageNet. Notably, Case 1 in Figure 4 is a small-bodied case in which the pancreas was not extracted correctly by full-scratch, whereas ImageNet and RadImageNet partially extracted it. However, FPs were observed in the pancreas using RadImageNet. In conclusion, ImageNet performs best among the three segmentation models. However, in contrast to the other organs, the pancreas had a high average of the FP rate in all three models. Nevertheless, the average FP rate was small compared to the average of the Dice index, and we believe that pancreas extraction performance has achieved acceptable accuracy. Several researchers have proposed various methods for organ segmentation, which is an important technique in the medical field. However, it is difficult to directly compare them because the number of training datasets used in each study and the quality of the images differ depending on the target modality. Table 3 presents existing studies using various methods. The proposed method showed the best results with the highest extraction accuracy in the experiments (DenseUNet, which was pre-trained using ImageNet). Notably, the proposed method achieved better extraction accuracy for all organs than did existing methods using PET/CT images.