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.

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.

公开(公告)号：US20230030424A1

公开(公告)日：2023-02-02

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: G06T7/00 ,  G06V10/82

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.