Deep learning-based color holographic microscopy.

Clicks: 245
ID: 41152
2019
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Abstract
We report a framework based on a generative adversarial network that performs high-fidelity color image reconstruction using a single hologram of a sample that is illuminated simultaneously by light at three different wavelengths. The trained network learns to eliminate missing-phase-related artifacts, and generates an accurate color transformation for the reconstructed image. Our framework is experimentally demonstrated using lung and prostate tissue sections that are labeled with different histological stains. This framework is envisaged to be applicable to point-of-care histopathology and presents a significant improvement in the throughput of coherent microscopy systems given that only a single hologram of the specimen is required for accurate color imaging.
Reference Key
liu2019deepjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Liu, Tairan;Wei, Zhensong;Rivenson, Yair;de Haan, Kevin;Zhang, Yibo;Wu, Yichen;Ozcan, Aydogan;
Journal Journal of biophotonics
Year 2019
DOI
10.1002/jbio.201900107
URL
Keywords Keywords not found

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