High-throughput phase-guided virtual staining using generalizable neural network

Kyung Chul Lee; Hyesuk Chae; Lucas Kreiss; Roarke Horstmeyer; Seung Ah Lee

doi:10.1117/12.3001057

13 March 2024 High-throughput phase-guided virtual staining using generalizable neural network

Kyung Chul Lee, Hyesuk Chae, Lucas Kreiss, Roarke Horstmeyer, Seung Ah Lee

Author Affiliations +

Proceedings Volume PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; PC128570I (2024) https://doi.org/10.1117/12.3001057
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

We propose a high-throughput phase-guided digital histological staining based on Fourier ptychographic microscopy using a generalizable deep neural network. Since the phase information includes the refractive index distribution of the specimen, we can digitally stain the unstained tissue slides from the quantitative phase images, which present the same color features that can be observed under a conventional microscope with the staining process. Here, we utilize Fourier Ptychographic Microscope that enables wide field and high-resolution quantitative phase imaging using multiple measurements by varying illumination angle. Additionally, we design a neural network that has remarkable generalization regarding sample dependence with the learned forward model. Along with this network architecture, we realize the efficient and effective digital staining process that does not require the labeled dataset from unstained tissue slides. We will report on the digital stained result from raw FPM images, the performance comparison, and discuss the future direction of our approach.

Conference Presentation

Citation Download Citation

Kyung Chul Lee, Hyesuk Chae, Lucas Kreiss, Roarke Horstmeyer, and Seung Ah Lee "High-throughput phase-guided virtual staining using generalizable neural network", Proc. SPIE PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, PC128570I (13 March 2024); https://doi.org/10.1117/12.3001057

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KEYWORDS

Neural networks

Nervous system

Phase reconstruction

Education and training

Network architectures

Microscopes

Reconstruction algorithms

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