# Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images
## 4 Discussion
In particular, the extraction accuracy of the proposed method was approximately 15% higher than that of the other methods for the pancreas. Furthermore, the extraction accuracy of the proposed method was comparable to those of existing studies that used diagnostic CT images of four different organs. However, there are existing studies that have achieved better extraction accuracy than the proposed  
Figure 5: Output areas with low extraction accuracy. (**a**) Input image; (**b**) PET/CT image; (**c**) \(\text{Candidate}_{\text{LDCT}}\); (**d**) \(\text{Candidate}_{\text{PET/CT}}\); (**e**) \(\text{Output}_{\text{proposed}}\). method for each of the four organs. We believe that the reason for this is that LDCT and PET images have significantly lower image contrast and spatial resolution than diagnostic contrast-enhanced CT images used in existing studies. Nevertheless, this method provided acceptable extraction accuracy because it used not only the morphological information obtained from the LDCT images but also the functional information obtained from the PET images. Moreover, soft-tissue organs neighboring other organs, such as the pancreas, have better contrast than LDCT images do, which is considered to provide useful information for organ segmentation. The proposed method demonstrates enhanced organ segmentation capabilities on low-quality and contrast PET/CT images, outperforming existing PET/CT studies in terms of extraction accuracy. Furthermore, its extraction accuracy aligns closely with that of established studies focusing on contrast-enhanced CT images for diagnostic applications. However, this study had three limitations. First, the data set used in this study was obtained at a single institution. Therefore, it is necessary to evaluate its robustness by conducting additional studies using data obtained at multiple institutions with different equipment and imaging protocols. However, since there is no open data set of PET/CT images for which organ labels have been created, it will be necessary to collaborate with other institutions to create image data and organ labels to evaluate the robustness and feasibility of the proposed method. Second, two-dimensional processing was used for organ segmentation. By extending the proposed method to 3D processing, information regarding the direction of the body axis can be retained, and more accurate organ extraction is expected. Third, only U-Net or DenseUNet was used in this study, thus the validation is limited. Today, convolutional neural network architectures for medical-image segmentation are being developed. Therefore, we believe that the effectiveness of the proposed method can be further proven by using those state-of-the-art architectures. Furthermore, in clinical practice, the extraction of organ regions is used in treatment planning for radiotherapy. Therefore, as a future study, we would like to compare the results of the proposed method with the accuracy of organ region extraction performed by trained workers in order to conduct a quantitative evaluation more in line with clinical practice.