Deep learning‐based color holographic microscopy. Issue 11 (1st August 2019)

• Record Type: Journal Article
• Authors: Liu, Tairan; Wei, Zhensong; Rivenson, Yair; de Haan, Kevin; Zhang, Yibo; Wu, Yichen; Ozcan, Aydogan
• Is Part Of: Journal of biophotonics. Volume 12:Issue 11(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-08-01
• Subjects: color holography -- computational microscopy -- deep learning -- digital holography -- neural networks; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.201900107
• Languages: English
• ISSNs: 1864-063X

Abstract

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. Abstract : A framework based on deep learning that performs high‐fidelity color image reconstruction using a single hologram of a sample that is illuminated simultaneously at three different wavelengths is reported. This framework significantly increases the throughput for coherent imaging systems and provides a practical, cost‐effective and compact alternative for resource‐limited settings.