公开(公告)号：US20220268754A1

公开(公告)日：2022-08-25

申请号：US17627400

申请日：2020-07-29

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Kevin de Haan ,  Hatice Ceylan Koydemir ,  Sabiha Tok ,  Derek Tseng

IPC: G01N33/18 ,  G01N21/64 ,  G02B6/04 ,  F21V8/00 ,  G01N21/78

Abstract: A system for detecting the presence of E. coli and total coliform in a water sample includes a sample holder that holds smaller, divided volumes of the sample and a testing reagent. A plurality of light sources are disposed above sample holder. The divided sample volumes are are illuminated with first and second light sources emitting light at different wavelengths. A bundle of optical fibers is provided with having an input end located adjacent to the divided sample volumes and is configured to receive light passing through the sample volumes. Light is output from the bundle of optical fibers and is captured with a camera. Image processing software is provided and is configured to calculate a light intensity in first and second wavelength channels at different times and outputs a positive/negative indication for E. coli and total coliform for the water sample.

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.

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：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.

公开(公告)号：US12190478B2

公开(公告)日：2025-01-07

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 ,  G06N3/08 ,  G06T3/4046 ,  G06T3/4053 ,  G06T3/4076 ,  G06T5/70 ,  G06T5/73 ,  G06T5/92 ,  G06T7/00

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.

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.

公开(公告)号：US20190333199A1

公开(公告)日：2019-10-31

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.