한빛사논문
- 조회304
Hyoungjun Park 1, Myeongsu Na2, Bumju Kim3, Soohyun Park4, Ki Hean Kim 3,4, Sunghoe Chang 2,5 & Jong Chul Ye 1,6,*
1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. 2Department of Physiology and Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea. 3Division of Integrative Biosciences and Biotechnology, Pohang University of Science and Technology, Pohang, South Korea. 4Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, South Korea. 5Neuroscience Research Institute, Seoul National University College of Medicine, Seoul, South Korea. 6 Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
*Corresponding author.
Abstract
Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution, in which the axial resolution is inferior to the lateral resolution. To address this problem, we present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in volumetric fluorescence microscopy. In contrast to the existing deep learning approaches that require matched high-resolution target images, our method greatly reduces the effort to be put into practice as the training of a network requires only a single 3D image stack, without a priori knowledge of the image formation process, registration of training data, or separate acquisition of target data. This is achieved based on the optimal transport-driven cycle-consistent generative adversarial network that learns from an unpaired matching between high-resolution 2D images in the lateral image plane and low-resolution 2D images in other planes. Using fluorescence confocal microscopy and light-sheet microscopy, we demonstrate that the trained network not only enhances axial resolution but also restores suppressed visual details between the imaging planes and removes imaging artifacts.
논문정보
- Research article
- 2022년 06월 (BRIC 등록일 2022-10-28)
- 국내 연구진
- 바이오·의료융합 > 의료기기
댓글 작성 로그인
팝업 닫기관련 링크
연구자 키워드
연구자 ID
관련분야 연구자보기
소속기관 논문보기
관련분야 논문보기
해당논문 저자보기
