# Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation
## 3 Methodology
### Attention Gate
The gray components in these sub-networks have been deleted during inference. in the CHAOS dataset: liver, right kidney, left kidney, and spleen. In this segmentation experiment, we considered the left and right kidney as the whole kidney and train three kinds of organs separately. The DSB [33] dataset contains 670 labeled nuclei scans. In DSB, we first rescaled the intensity (set min at 0 and max at 255) and transformed all the images into gray images. Then, we used the Otsu threshold [34] and inverted the intensity of images so the majority of pixels were above the threshold. Moreover, we did not use any clustering, thinking that training a single model would lead to better generalization. In order to remove irrelevant details in the image, we only take the Hounsfield Unit value in the range of [\(-200,200\)] in the image. We take one-fifth of all annotated cases as the validation dataset, and the rest as the training dataset. ### Optimization Techniques and Performance Evaluation

The proposed model is an end-to-end model, which means that we did not use too many preprocessing methods and denoising techniques. We just modified the size of the input image to meet the requirements of the network. The entire project is based on pytorch for deep learning programming and application. We used Nvidia GeForce GTX1080 Ti for network training. During training, we used the hybrid loss function proposed in Section 3.4 for back propagation and network parameters update. In addition, [PERSON] is selected as the optimizer, the learning rate is initially 0.01, and decreases to 0.0001 with training. In the optimizer, we also added a braking mechanism. When the training loss has been 20 epochs and no longer decreases, we will interrupt the training. In the organ segmentation experiment, we used five popular indicators in medical image segmentation tasks to evaluate segmentation quality, including dice coefficient (Dice) [35], mean intersection over union (mIoU) [36], precision [37], recall [38], and Hausdorff distance [39]. mIoU is the mean value of IoU obtained in the segmentation experiment for each class in the current dataset. The mathematical expressions of the above metrics are:

Figure 3.2

Figure 3.3

Figure 3.4

Figure 3.5

Figure 3.6

where \(TP\) is the True Positive, \(FP\) is the False Positive, \(FN\) is the False Negative. \(Y\) means the segmentation result when \(\bar{Y}\) is the annotated result. The \(d()\) means the calculation of Manhattan distance. The Hausdorff distance is added for evaluation of the segmentation on boundary. After the mask output by the model is obtained, the pixel value of the organ area is 1, and the pixel value of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **Resolution** & **Modality** & **Provider** \\ \hline Liver & 256 \(\times\) 256 & CT & 2017 MICCAI LiTS \\ Spleen & 256 \(\times\) 256 & MRI & 2019 ISBI CHAOS \\ Kidney & 256 \(\times\) 256 & MRI & 2019 ISBI CHAOS \\ Liver & 256 \(\times\) 256 & MRI & 2019 ISBI CHAOS \\ Nuclei & 96 \(\times\) 96 & Mixed & 2018 DSB Kaggle \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An overview of some important information of the medical image datasets used in this experiment. background area is 0. When calculating the Hausdorff distance, we only consider the boundary of the object. In addition, the \(\in\) denotes the boundary of the object. As the values of the first four indicators rise, the similarity increases, and the accuracy of segmentation improves. ### Feature Visualization

In Sections 3.1 and 3.4, we introduced the nested convolution structure and dense skip connection into Render U-Net. In this section, we display the features of the first layer, which intuitively confirms the superiority of the network structure for semantic feature extraction. As we can see in Figure 8, we used four different networks for the liver MRI image segmentation task. Then, we visualized the features of the first layer \(X_{0\_N}\)\((N\in[0,1,2,3,4])\).