# Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images
## 2 Methods
### Organ Segmentation  
In this study, we used two types of segmentation networks. The first was a custom version of U-Net [17], which was proposed by [PERSON] et al. In 2015 for segmenting medical images. U-Net consists of three blocks: encoder, bottleneck, and decoder. In general, when multiple convolutional integrations are performed in a CNN, downsampling is likely to result in a loss of spatial information. However, U-Net has a skip connection that combines the feature map obtained by the encoder with that obtained by the decoder, thereby enabling the preservation of local and spatial positional information, which is important in medical imaging. In this study, we used a customized network of the original U-Net. A regularized linear unit (ReLU) was used as the activation function. Batch regularization was also used as a regularization technique to stabilize learning and improve generalization. And maximum pooling was used for downsampling and nearest interpolation for upsampling. Furthermore, LDCT images only and LDCT and PET images were provided to U-Net, and two types of organ regions were outputted. When inputting the LDCT and PET images, each image was divided into separate channels and input into U-Net. However, this network's weights are randomly initialized, which reduces the learning stability for a small number of datasets. Notably, this study used fewer datasets to train the network than did existing studies. Therefore, we aimed to achieve highly accurate organ extraction from a small number of datasets using pre-training.  
For the second network, DenseUNet with a DenseNet121 encoder was used for pre-training. DenseNet121 has been reported to perform well as a feature extractor in organ segmentation networks [18]. Moreover, the sizes of the input and output images and the input method in DenseUNet were the same as those in U-Net, as shown in the first example. Notably, in many existing studies using deep learning, ImageNet, which consists of natural images, has been used as a pre-training dataset. However, better results have been obtained for medical studies using a pre-training dataset consisting of medical images. Hence, within this study, we compared the full-scratch model, which begins with random weight initialization, to the fine-tuning model. The latter initializes network weights using both ImageNet and RadImageNet weights.  
The combination loss [19] reported by [PERSON] et al. was used as the loss function. Furthermore, combo loss consists of Dice loss and weighted cross-entropy loss and has two parameters, \(\alpha\) and \(\beta\). \(\alpha\) adjusts the weights of Dice loss and weighted entropy loss, while \(\beta\) affects weighted cross-entropy loss and can change the sensitivity of the output. In this study, \(\alpha=0.5\), which is similar to that of the study by [PERSON] et al. Furthermore, the combo loss is more strongly penalized for false negatives when the value of \(\beta\) is set to a value higher than 0.5.  
PET/CT images were acquired with a wide FOV; therefore, the organ area was smaller than that of the input image. Thus, the output tends to be underestimated in cases with small body sizes and in the pancreas, which has a small organ area and large individual differences. Therefore, we performed a grid search in the range of \(\beta=0.5\) to 1.0 and selected \(\beta=0.6\), which gave the best results.