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    Systems and methods for deep learning microscopy
    1.
    发明授权
    Systems and methods for deep learning microscopy 有权

    公开(公告)号：US11222415B2

    公开(公告)日：2022-01-11

    申请号：US16395674

    申请日：2019-04-26

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Hongda Wang ,  Harun Gunaydin ,  Kevin de Haan

    IPC: G06T5/50 ,  G06N3/08 ,  G06T3/40 ,  G06T5/00

    Abstract: A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and/or image contrast.

    METHOD AND SYSTEM FOR DIGITAL STAINING OF LABEL-FREE FLUORESCENCE IMAGES USING DEEP LEARNING
    4.
    发明申请
    METHOD AND SYSTEM FOR DIGITAL STAINING OF LABEL-FREE FLUORESCENCE IMAGES USING DEEP LEARNING 有权

    公开(公告)号：US20210043331A1

    公开(公告)日：2021-02-11

    申请号：US17041447

    申请日：2019-03-29

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Hongda Wang ,  Zhensong Wei

    IPC: G16H70/60 ,  G16H30/20 ,  G16H30/40 ,  G06N3/08 ,  G06K9/62

    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. This label-free digital staining method eliminates cumbersome and costly histochemical staining procedures and significantly simplifies tissue preparation in pathology and histology fields.

    Method and system for digital staining of microscopy images using deep learning
    6.
    发明授权
    Method and system for digital staining of microscopy images using deep learning 有权

    公开(公告)号：US12300006B2

    公开(公告)日：2025-05-13

    申请号：US17783260

    申请日：2020-12-22

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Hongda Wang ,  Yilin Luo ,  Kevin de Haan ,  Yijie Zhang ,  Bijie Bai

    IPC: G06V20/00 ,  G06T7/00 ,  G06V10/82 ,  G06V20/69

    Abstract: A deep learning-based digital/virtual staining method and system enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples. In one embodiment, the method of generates digitally/virtually-stained microscope images of label-free or unstained samples using fluorescence lifetime (FLIM) image(s) of the sample(s) using a fluorescence microscope. In another embodiment, a digital/virtual autofocusing method is provided that uses machine learning to generate a microscope image with improved focus using a trained, deep neural network. In another embodiment, a trained deep neural network generates digitally/virtually stained microscopic images of a label-free or unstained sample obtained with a microscope having multiple different stains. The multiple stains in the output image or sub-regions thereof are substantially equivalent to the corresponding microscopic images or image sub-regions of the same sample that has been histochemically stained.

    SYSTEMS AND METHODS FOR DEEP LEARNING MICROSCOPY
    7.
    发明申请
    SYSTEMS AND METHODS FOR DEEP LEARNING MICROSCOPY 有权

    公开(公告)号：US20220114711A1

    公开(公告)日：2022-04-14

    申请号：US17530471

    申请日：2021-11-19

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Hongda Wang ,  Harun Gunaydin ,  Kevin de Haan

    IPC: G06T5/50 ,  G06T7/00 ,  G06T3/40

    Abstract: A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and/or image contrast.

    LABEL-FREE VIRTUAL IMMUNOHISTOCHEMICAL STAINING OF TISSUE USING DEEP LEARNING
    8.
    发明申请
    LABEL-FREE VIRTUAL IMMUNOHISTOCHEMICAL STAINING OF TISSUE USING DEEP LEARNING 有权

    公开(公告)号：US20250046069A1

    公开(公告)日：2025-02-06

    申请号：US18715333

    申请日：2022-11-30

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Bijie Bai ,  Hongda Wang

    IPC: G06V10/82 ,  G06V10/143 ,  G06V20/69

    Abstract: A deep learning-based virtual HER2 IHC staining method uses a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this staining framework was demonstrated by quantitative analysis of blindly graded HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs). A second quantitative blinded study revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory, and can be extended to other types of biomarkers to accelerate the IHC tissue staining and biomedical workflow.

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