Realistic visual rendering of endoscopy simulation using GAN (generative adversarial network) and real procedure images = 실제 시술 영상과 생성적 적대 신경망을 이용한 내시경 시뮬레이션의 현실감 높은 시각 렌더링

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High-quality visual feedback is important for an immersive medical simulation. There are several researches about rendering visual feedback in medical simulation based on deep learning and procedure images. These methods, however, are focusing on static surgeries with narrow variety of scenes. Endoscopy procedure includes dynamic movements of endoscopy, so it has wide variety of scenes. This paper proposes a deep learning based rendering method to provide photo-realistic visual feedbacks for users in endoscopy simulation. A transformation network based on generative adversarial networks (GAN) is designed and trained to learn a mapping function from simulation depth map to realistic visual feedbacks. Mapping function from real endoscopy procedure images to depth map is learned in the first place, and then the goal function is learned as an inverse function. Realism of visual feedbacks that generated by the proposed method are evaluated with NR-IQA method.
Advisors
Lee, Doo Yongresearcher이두용researcher
Description
한국과학기술원 :기계공학과,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 기계공학과, 2020.2,[72 p. :]

Keywords

Medical simulation▼aVisual feedback▼aDeep learning▼aGenerative adversarial networks (GAN)▼aRendering; 의료 시뮬레이션▼a시각 피드백▼a심층 학습▼a생성적 적대 신경망 (GAN)▼a렌더링

URI
http://hdl.handle.net/10203/284075
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=910888&flag=dissertation
Appears in Collection
ME-Theses_Master(석사논문)
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