Semantic Segmentation in Endoscopy Surgery: Using DataAugmentation to Train Deep Neural Net with Few Data

• Main Authors: Cheng-Shao Chiang, 蔣承劭
• Other Authors: Chi-Sheng Shih
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/6jzrh8

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 107 === As the computer-aided surgery getting popular, more and more research has been conducted to help surgeons operate. Most of the research are focusing on common tasks with respect to computer vision and trying to provide surgeons with more information by analyzing the images captured, whereas in this thesis, we aim at the semantic segmentation in the endoscopy surgery scenario because semantic segmentation is the first step for a computer to grasp what shows up in the vision of an endoscope. Although semantic segmentation is a popular research topic, most of the current algorithm focus on road’s scene, which needs myriads of training data. Since the data endoscopy surgery scene is relatively scarce, the performance of existing algorithms is thus rather limited.Therefore, we tried to solve the problem of training a semantic segmentation network with few data in this work. We propose a data augmentation method that can synthesize new training data. The experiment results show that our method can improve the performance in recognizing anatomical objects effectively.