AI in EE

AI IN DIVISIONS

AI in Signal Division

AI in EE

AI IN DIVISIONS

AI in Signal Division ​ ​

AI in Signal Division

RLP-Net: A Recursive Light Propagation Network for 3-D Virtual Refocusing

Title : RLP-Net: A Recursive Light Propagation Network for 3-D Virtual Refocusing

Author : Changyeop Shin*, Hyun Ryu*, Eun-Seo Cho, Young-Gyu Yoon (* co-first authors)

Journal/Conference and Year : International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021 (Selected for MICCAI Young Scientist Award, MICCAI Student Travel Award, and oral presentation)

Abstract : High-speed optical 3-D fluorescence microscopy is an essential tool for capturing the rapid dynamics of biological systems such as cellular signaling and complex movements. Designing such an optical system is constrained by the inherent trade-off among resolution, speed, and noise which comes from the limited number of photons that can be collected. In this paper, we propose a recursive light propagation network (RLP-Net) that infers the 3-D volume from two adjacent 2-D wide-field fluorescence images via virtual refocusing. Specifically, we propose 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. Experimental results show that the proposed method can faithfully reconstruct the 3-D volume from two planes in terms of both quantitative measures and visual quality. The source code used in the paper is available at https://github.com/NICALab/rlpnet.