We report a deep learning-based colorization framework for holographic microscopy, and demonstrate its efficacy by imaging histopathology slides (Masson’s trichrome-stained lung and H&E-stained prostate tissue). Using a generative adversarial network, this framework is trained to eliminate the missing-phase-related artifacts. To obtain accurate color information, the pathology slides were imaged under multiplexed illumination at three wavelengths, and the deep network learns to demultiplex and project the holographic images from the three color channels into the RGB color-space, achieving high color-fidelity. Our method dramatically simplifies the data acquisition and shortens the processing time, which is important for e.g., digital pathology in resource-limited-settings.

Date Published: 9 March 2020
PDF
Proc. SPIE 11230, Optics and Biophotonics in Low-Resource Settings VI, 112300T (9 March 2020); doi: 10.1117/12.2546956

Published in SPIE Proceedings Vol. 11230:
Optics and Biophotonics in Low-Resource Settings VI
David Levitz; Aydogan Ozcan, Editor(s)