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Open AccessArticle

Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation

by Chen Li, Wei Chen *,† and Yusong Tan
College of Computer, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2020, 10(18), 6439; https://doi.org/10.3390/app10186439
Received: 20 August 2020 / Revised: 7 September 2020 / Accepted: 11 September 2020 / Published: 16 September 2020
(This article belongs to the Special Issue Artificial Intelligence for Medical Image Analysis)
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. View Full-Text
Keywords: semantic segmentation; rendering; medical image; artificial intelligence; deep learning semantic segmentation; rendering; medical image; artificial intelligence; deep learning
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Li, C.; Chen, W.; Tan, Y. Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation. Appl. Sci. 2020, 10, 6439.

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