한빛사논문
- 조회236
Changyeop Shina,1, Hyun Ryua,1, Eun-Seo Choa, Seungjae Hana, Kang-Han Leeb, Cheol-Hee Kimb, Young-Gyu Yoona,c
aSchool of Electrical Engineering, KAIST, 291, Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
bDepartment of Biology, Chungnam National University, Daejeon, Republic of Korea
cKAIST Institute for Health Science and Technology, 291, Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
Corresponding author : Young-Gyu Yoon
Abstract
Three-dimensional fluorescence microscopy has an intrinsic performance limit set by the number of photons that can be collected from the sample in a given time interval. Here, we extend our earlier work – a recursive light propagation network (RLP-Net) – which is a computational microscopy technique that overcomes such limitations through virtual refocusing that enables volume reconstruction from two adjacent 2-D wide-field fluorescence images. RLP-Net employs a recursive inference scheme in which the network progressively predicts the subsequent planes along the axial direction. This recursive inference scheme reflects that the law of physics for the light propagation remains spatially invariant and therefore a fixed function (i.e., a neural network) for a short distance light propagation can be recursively applied for a longer distance light propagation. In addition, we employ a self-supervised denoising method to enable accurate virtual light propagation over a long distance. We demonstrate the capability of our method through high-speed volumetric imaging of neuronal activity of a live zebrafish brain. The source code used in the paper is available at https://github.com/NICALab/rlpnet.
논문정보
- Research article
- 2022년 11월 (BRIC 등록일 2023-01-27)
- 국내 연구진
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