System and method for transforming holographic microscopy images to microscopy images of various modalities

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United States of America

PATENT NO 12020165
SERIAL NO

17294384

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A trained deep neural network transforms an image of a sample obtained with a holographic microscope to an image that substantially resembles a microscopy image obtained with a microscope having a different microscopy image modality. Examples of different imaging modalities include bright-field, fluorescence, and dark-field. For bright-field applications, deep learning brings bright-field microscopy contrast to holographic images of a sample, bridging the volumetric imaging capability of holography with the speckle-free and artifact-free image contrast of bright-field microscopy. Holographic microscopy images obtained with a holographic microscope are input into a trained deep neural network to perform cross-modality image transformation from a digitally back-propagated hologram corresponding to a particular depth within a sample volume into an image that substantially resembles a microscopy image of the sample obtained at the same particular depth with a microscope having the different microscopy image modality.

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Patent OwnerAddress
THE REGENTS OF THE UNIVERSITY OF CALIFORNIA1111 FRANKLIN STREET TWELFTH FLOOR OAKLAND CA 94607-5200

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Ozcan, Aydogan Los Angeles, US 103 2065
Rivenson, Yair Los Angeles, US 29 211
Wu, Yichen Los Angeles, US 14 165

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