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Shin et al., 2022 - Three-dimensional fluorescence microscopy through virtual refocusing using a recursive light propagation network

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Fig. 2

Fig. 2. (a) RLP-Net architecture. The network consists of 8 residual and dense blocks (RDBs) and a memory module (MM) in the middle. It takes two adjacent image planes and produces the subsequent plane in a single step. The hidden state of the MM (which is a Conv-LSTM) is used for the inference in the next axial step. (b) 3-D virtual refocusing with RLP-Net. The 3-D volume is reconstructed through recursive inferences using two adjacent images. After the recursive inference, the input images $I_{0}$ $I_{0}$ and $I_{1}$ $I_{1}$ are swapped to perform the inference towards the other side (as the concatenation order of two inputs determines the inference direction). (c) RLP-Net training procedure. The loss function is evaluated based on the inference results where the number of recursive inference steps is gradually increased so that the network can progressively learn to propagate the image further. The cycle loss was introduced to exploit the additive property of the light propagation function.

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Reprinted from Medical image analysis, 82, Shin, C., Ryu, H., Cho, E.S., Han, S., Lee, K.H., Kim, C.H., Yoon, Y.G., Three-dimensional fluorescence microscopy through virtual refocusing using a recursive light propagation network, 102600, Copyright (2022) with permission from Elsevier. Full text @ Med. Image Anal.