Deep learning-based color holographic microscopy

Abstract

We report a framework based on a generative adversarial network (GAN) 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.

25 pages, 8 Figures, 2 Tables

References in corpus (7)

• Phase recovery and holographic image reconstruction using deep learning in neural networks
• Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue
• PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning
• Resolution enhancement in scanning electron microscopy using deep learning
• Cross-modality deep learning brings bright-field microscopy contrast to holography
• Deep learning-based super-resolution in coherent imaging systems
• Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains

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• Label-free evaluation of lung and heart transplant biopsies using tissue autofluorescence-based virtual staining
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