# Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation
###### Abstract  
Organ lesions have a high mortality rate, and pose a serious threat to people's lives. Segmenting organs accurately is helpful for doctors to diagnose. There is a demand for the advanced segmentation model for medical images. However, most segmentation models directly migrated from natural image segmentation models. These models usually ignore the importance of the boundary. To solve this difficulty, in this paper, we provided a unique perspective on rendering to explore accurate medical image segmentation. We adapt a subdivision-based point-sampling method to get high-quality boundaries. In addition, we integrated the attention mechanism and nested U-Net architecture into the proposed network Render U-Net.Render U-Net was evaluated on three public datasets, including LiTS, CHAOS, and DSB. This model obtained the best performance on five medical image segmentation tasks. s 202020  
good results. In 2015, [PERSON] [9] and [PERSON] [10] provided other effective ways (FCN and U-Net) to overcome this difficulty. Since then, there have been many studies [11; 12] and applications [13] based on these two network architectures. Unfortunately, these models usually pay more attention to the overall characteristics of the target, but ignore the importance of boundaries in medical image segmentation. Such designs are likely to result in a high evaluation indicator (e.g., Dice and IoU), but this high indicator does not represent accurate segmentation because it does not perform well on boundary. Prediction errors at the boundaries will not have any influence on segmentation of natural images, but it is completely different when segmenting medical images. The rigor of medicine requires greater accuracy of boundaries, especially in segmentation of human organs. Even a small boundary error is very likely to cause a larger medical accident. Therefore, there is an urgent demand for accurate segmentation for medical images. To optimize the segmentation performance at the boundary, we returned to the essence of image analysis and looked for strategies. In the field of image analysis, images are often regarded as regular grids of pixels (points). Their feature vectors are hidden representations on the regular grid of the image. In image semantic segmentation, the pixels in the hidden representation will be mapped to a set of labels after uniform upsampling. These labels are used as output masks to indicate the predicted category at each pixel. Because the pixels in boundaries are only small percentages of the entire image, the proportion of the object interior is relatively large and smooth. Thus, the original up-sampling method will unnecessarily oversample the object interior but undersample the object boundary. Image segmentation methods often upsample regular grids on a low resolution, which is a compromise between undersampling and oversampling. This is one important reason for blurry boundary. To solve this difficulty, we turned to the rendering method in computer graphics for help. The rendering method does not calculate the labels uniformly on the hidden representation. Rather, it adaptively selects uncertain points from all pixels of the image to calculate labels. For example, the subdivision strategy [14] can effectively render anti-aliased high-resolution images. This is the difference between rendering and upsampling. As a consequence, we provide a unique perspective on render and propose a network based on nested UNet, named **Render U-Net**. Our work is to explore the challenges of accurate medical image segmentation, which can be summarized as follows:  
1. Render U-Net draws on the idea of render in computer graphics. For \"rendering\" high-quality boundaries, Render U-Net adapts a subdivision-based point-sampling method to replace the original upsampling method (Section 3.3). 2. Render U-Net integrates the Attention mechanism (Section 3.2) into the nested U-Net architecture (Section 3.1), and it introduces deep supervision (Section 3.4) to supervise the extraction of semantic information at all levels.