公开(公告)号：US12106552B2

公开(公告)日：2024-10-01

申请号：US17261542

申请日：2019-03-29

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Zhensong Wei

IPC: G06V10/82 ,  G06F18/214 ,  G06N3/08 ,  G06V10/764 ,  G06V20/69

CPC classification number: G06V10/82 ,  G06F18/2148 ,  G06F18/2155 ,  G06N3/08 ,  G06V10/764 ,  G06V20/693 ,  G06V20/698

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.

公开(公告)号：US20240135544A1

公开(公告)日：2024-04-25

申请号：US18543168

申请日：2023-12-18

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

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

IPC: G06T7/11 ,  G06F18/214 ,  G06N3/08 ,  G06V10/764 ,  G06V10/82 ,  G16H30/20 ,  G16H30/40 ,  G16H70/60

CPC classification number: G06T7/11 ,  G06F18/2155 ,  G06N3/08 ,  G06V10/764 ,  G06V10/82 ,  G16H30/20 ,  G16H30/40 ,  G16H70/60

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.

公开(公告)号：US11893739B2

公开(公告)日：2024-02-06

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: G06T7/11 ,  G16H70/60 ,  G16H30/20 ,  G16H30/40 ,  G06N3/08 ,  G06F18/214 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06T7/11 ,  G06F18/2155 ,  G06N3/08 ,  G06V10/764 ,  G06V10/82 ,  G16H30/20 ,  G16H30/40 ,  G16H70/60

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.

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.

公开(公告)号：US20220206434A1

公开(公告)日：2022-06-30

申请号：US17604416

申请日：2020-04-21

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Tairan Liu ,  Yibo Zhang ,  Zhensong Wei

IPC: G03H1/08 ,  G03H1/04 ,  G03H1/26 ,  G06N3/08 ,  G06N3/04

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.

公开(公告)号：US20210264214A1

公开(公告)日：2021-08-26

申请号：US17261542

申请日：2019-03-29

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Zhensong Wei

IPC: G06K9/62 ,  G06N3/08 ,  G06K9/00

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.