Artificial confocal microscopy for deep label-free imaging

Xi Chen; Mikhail E. Kandel; Shenghua He; Chenfei Hu; Young Jae Lee; Kathryn Sullivan; Gregory Tracy; Hee Jung Chung; Hyun Joon Kong; Mark Anastasio; Gabriel Popescu

doi:10.1117/12.3001172

13 March 2024 Artificial confocal microscopy for deep label-free imaging

Xi Chen, Mikhail E. Kandel, Shenghua He, Chenfei Hu, Young Jae Lee, Kathryn Sullivan, Gregory Tracy, Hee Jung Chung, Hyun Joon Kong, Mark Anastasio, Gabriel Popescu

Author Affiliations +

Proceedings Volume PC12852, Quantitative Phase Imaging X; PC128520V (2024) https://doi.org/10.1117/12.3001172
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

We present artificial confocal microscopy (ACM) to achieve confocal-level depth sectioning, sensitivity, and chemical specificity non-destructively on unlabeled specimens. ACM is equipped with a laser scanning confocal microscopy with a quantitative phase imaging module, which provides optical path-length maps of the specimen colocalized with the fluorescence channel. Using pairs of phase and fluorescence images, a convolution neural network was trained to translate the former into the latter. The ACM images hold much stronger depth sectioning than the input (phase) images, enabling us to recover confocal-like tomographic volumes of microspheres, hippocampal neurons in culture, and three-dimensional liver cancer spheroids.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xi Chen, Mikhail E. Kandel, Shenghua He, Chenfei Hu, Young Jae Lee, Kathryn Sullivan, Gregory Tracy, Hee Jung Chung, Hyun Joon Kong, Mark Anastasio, and Gabriel Popescu "Artificial confocal microscopy for deep label-free imaging", Proc. SPIE PC12852, Quantitative Phase Imaging X, PC128520V (13 March 2024); https://doi.org/10.1117/12.3001172

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