Convolutional recurrent neural network-enabled volumetric fluorescence imaging

Luzhe Huang; Hanlong Chen; Yilin Luo; Yair Rivenson; Aydogan Ozcan

doi:10.1117/12.2594411

1 August 2021 Convolutional recurrent neural network-enabled volumetric fluorescence imaging

Luzhe Huang, Hanlong Chen, Yilin Luo, Yair Rivenson, Aydogan Ozcan

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 1180411 (2021) https://doi.org/10.1117/12.2594411
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

We report a recurrent neural network (RNN)-based cross-modality image inference framework, termed Recurrent-MZ+, that explicitly incorporates two or three 2D fluorescence images, acquired at different axial planes, to rapidly reconstruct fluorescence images at arbitrary axial positions within the sample volume, matching the 3D image of the same sample acquired with a confocal scanning microscope. We demonstrated the efficacy of Recurrent-MZ+ on transgenic C. Elegans samples; using 3 wide-field fluorescence images as input, the reconstructed sample volume by Recurrent-MZ+ mitigates the deformations caused by the anisotropic point-spread-function of wide-field microscopy, and matches the ground truth confocal image stack of the sample.

Conference Presentation

Citation Download Citation

Luzhe Huang, Hanlong Chen, Yilin Luo, Yair Rivenson, and Aydogan Ozcan "Convolutional recurrent neural network-enabled volumetric fluorescence imaging", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 1180411 (1 August 2021); https://doi.org/10.1117/12.2594411

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