# Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images
## 1 Introduction
### Related Works  
Many researchers have proposed various methods for organ segmentation in diagnostic CT images. [PERSON] et al. and [PERSON] et al. proposed an atlas-based organ segmentation method [6; 7]. However, the accuracy of organ extraction in this model is highly dependent on the accuracy of the image registration, which requires a wide variety of atlas datasets. Moreover, [PERSON] et al. and [PERSON] et al. proposed machine learning-based methods [8; 9], which are beneficial for detecting and estimating the location of anatomical structures; however, manually set features are important, and setting the appropriate features represents a heavy burden. Recently, segmentation methods based on deep learning that can automatically learn features obtained from image data have become the standard segmentation methods.  
[PERSON] et al. proposed an end-to-end 3D convolutional neural network (CNN)-based segmentation method [10]. In that study, the number of training cases changed, suggesting the importance of the number of training cases in deep learning-based methods. Furthermore, [PERSON] et al. proposed a fully convolutional network (FCN) voting method that performed organ segmentation from axial, coronal, and sagittal cross-sections and combined the results of these three cross-sections for each pixel value [11]. The training data were increased using three cross-sections, and organ segmentation was performed considering the organ information in the body's axial direction. Furthermore, [PERSON] et al. proposed organ segmentation focusing on small organs such as the pancreas and gallbladder, which are difficult to extract, using multiple 3D FCNs in stages [12]. These existing studies have utilized CT images, contrast-enhanced CT images with a sufficient dose on a diagnostic CT scanner, or MR images, and various segmentation techniques that can extract organ regions with high accuracy have been developed.  
In contrast, there are few studies on organ segmentation for LDCT images obtained using a PET/CT scanner. In an existing study on LDCT images, [PERSON] et al. proposed a multi-atlas segmentation (MAS) framework [13]. Specifically, the organs were segmented for LDCT images using the MAS method, and the abdominal region of the PET image was automatically cropped. Next, MAS was applied to the fused PET and LDCT images to accurately extract the organs. Notably, [PERSON] et al. proposed an organ segmentation method using deep learning [14]. Their endeavor involved utilizing two 3 DVNets in a stepwise process to achieve more accurate organ extraction from processed LDCT images. However, when compared to studies on diagnostic CT images, the accuracy of extraction in existing PET/CT image studies was inadequate. This emphasizes the need to focus on improving extraction accuracy. LDCT images are acquired without contrast agents and at low doses; thus, they have a lower resolution than diagnostic CT images, and the margins of the organs are unclear. In addition, because LDCT images are used for attenuation correction of PET images they are captured with a wide field of view (FOV), and the proportion of organs in the FOV is small. Therefore, highly accurate organ segmentation for PET/CT images is more difficult than it is for diagnostic CT images. Therefore, improving the extraction accuracy of organ segmentation methods for PET/CT images is necessary.