Patents by Inventor Zhensong Wei

Zhensong Wei has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20220206434
    Abstract: A method for performing color image reconstruction of a single super-resolved holographic sample image includes obtaining a plurality of sub-pixel shifted lower resolution hologram images of the sample using an image sensor by simultaneous illumination at multiple color channels. Super-resolved hologram intensity images for each color channel are digitally generated based on the lower resolution hologram images. The super-resolved hologram intensity images for each color channel are back propagated to an object plane with image processing software to generate a real and imaginary input images of the sample for each color channel. A trained deep neural network is provided and is executed by image processing software using one or more processors of a computing device and configured to receive the real input image and the imaginary input image of the sample for each color channel and generate a color output image of the sample.
    Type: Application
    Filed: April 21, 2020
    Publication date: June 30, 2022
    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
    Inventors: Aydogan Ozcan, Yair Rivenson, Tairan Liu, Yibo Zhang, Zhensong Wei
  • Publication number: 20210264214
    Abstract: A deep learning-based digital staining method and system are disclosed that provides a label-free approach to create a virtually-stained microscopic images from quantitative phase images (QPI) of label-free samples. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses a convolutional neural network trained using a generative adversarial network model to transform QPI images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample. This label-free digital staining method eliminates cumbersome and costly histochemical staining procedures, and would significantly simplify tissue preparation in pathology and histology fields.
    Type: Application
    Filed: March 29, 2019
    Publication date: August 26, 2021
    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
    Inventors: Aydogan Ozcan, Yair Rivenson, Zhensong Wei
  • Publication number: 20210043331
    Abstract: A deep learning-based digital staining method and system are disclosed that enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples based on autofluorescence images acquired using a fluorescent microscope. The system and method have particular applicability for the creation of digitally/virtually-stained whole slide images (WSIs) of unlabeled/unstained tissue samples that are analyzes by a histopathologist. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses, in one embodiment, a convolutional neural network trained using a generative adversarial network model to transform fluorescence images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample.
    Type: Application
    Filed: March 29, 2019
    Publication date: February 11, 2021
    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
    Inventors: Aydogan Ozcan, Yair Rivenson, Hongda Wang, Zhensong Wei