公开(公告)号：US20250037485A1

公开(公告)日：2025-01-30

申请号：US18782178

申请日：2024-07-24

Applicant: Leica Microsystems CMS GmbH

Inventor： Constantin KAPPEL ,  José Miguel SERRA LLETI ,  Volker SCHWEIKHARD ,  Hoyin LAI

IPC: G06V20/69 ,  G06T7/12 ,  G06T7/50 ,  G06V20/70 ,  G16H30/40

Abstract: An image processing system is configured to receive at least one reference image, wherein each reference image is a microscopy image capturing cells of a biological sample, wherein the at least one reference image includes at least one reference labeling directed to a reference cellular compartment of the captured cells. The image processing system is configured to employ a trained deep neural network for processing the at least one reference image to generate a target image, wherein the target image includes a target labeling directed to a target cellular compartment of the captured cells, wherein the at least one reference labeling comprises a fluorescence labeling, wherein the reference cellular compartment is a distributed structure within cells, and wherein the target cellular compartment is the cell nucleus.

公开(公告)号：US20220343463A1

公开(公告)日：2022-10-27

申请号：US17413590

申请日：2019-12-11

Applicant: LEICA MICROSYSTEMS CMS GMBH

Inventor： Constantin KAPPEL

IPC: G06T3/40 ,  G06T5/00 ,  G06T5/50

Abstract: An apparatus for scaling images includes one or more processors and one or more computer-readable storage media on which computer-executable instructions are stored. The computer-executable instructions, upon being executed by the one or more processors, provide for execution of the following steps: capturing one or more first images by an imaging and/or image recording system, wherein the one or more captured first images are related to a first resolution; and generating, by a neural network, one or more corresponding second images based on one or more captured first images, wherein the one or more second images are related to a second resolution, the first resolution differing from the second resolution.

公开(公告)号：US20220229862A1

公开(公告)日：2022-07-21

申请号：US17596289

申请日：2019-06-07

Applicant: Leica Microsystems CMS GmbH

Inventor： Constantin KAPPEL

IPC: G06F16/33 ,  G06K9/62 ,  G06F16/583 ,  G16H30/20 ,  G06N3/04 ,  G06N3/08 ,  G16H30/40 ,  G06F40/40

Abstract: Embodiments relate to a system (100) comprising one or more processors (110) and one or more storage devices (120). The system (100) is configured to receive biology-related language-based search data (101) and generate a first high-dimensional representation of the biology-related language-based search data (101) by a trained language recognition ma-chine-learning algorithm executed by the one or more processors (110). The first high-dimensional representation comprises at least 3 entries each having a different value. Further, the system is configured to obtain a plurality of second high-dimensional representations (105) of a plurality of biology-related image-based input data sets or of a plurality of biology-related language-based input data sets and compare the first high-dimensional representation with each second high-dimensional representation of the plurality of second high-dimensional representations (105).

公开(公告)号：US20230011970A1

公开(公告)日：2023-01-12

申请号：US17810844

申请日：2022-07-06

Applicant: Leica Microsystems CMS GmbH

Inventor： José Miguel SERRA LLETI ,  Constantin KAPPEL

IPC: G16B40/20 ,  G16B5/00

Abstract: An embodiment of a method 100 for predicting a future state of a biological system is provided. The method 100 comprises receiving 101a microscope image depicting the biological system at an associated time and receiving 102 metadata corresponding to the microscope image. The method 100 further comprises extracting 103 features from the microscope image having information on a state of the biological system and using 104 the features and the metadata to predict the future state of the biological system.

公开(公告)号：US20220028116A1

公开(公告)日：2022-01-27

申请号：US17294692

申请日：2019-11-20

Applicant: LEICA MICROSYSTEMS CMS GMBH

Inventor： Frank SIECKMANN ,  Constantin KAPPEL

IPC: G06T7/80 ,  G02B21/36 ,  G02B21/24 ,  G06N3/04

Abstract: A method for determining a focus position includes recording at least one first image, wherein image data of the at least one recorded first image are dependent on at least one first focus position during the recording of the at least one first image. A second focus position is determined based on an analysis of the at least one recorded first image using a trained model. At least one second image is recorded using the second focus position. The at least one first image and the at least one second image contain items of information which are in a context with a training of the trained model.

公开(公告)号：US20210342636A1

公开(公告)日：2021-11-04

申请号：US17285477

申请日：2019-09-25

Applicant: LEICA MICROSYSTEMS CMS GMBH

Inventor： Frank SIECKMANN ,  Constantin KAPPEL

IPC: G06K9/62 ,  G06N20/00 ,  G02B21/36

Abstract: A method for optimizing a workflow of at least one microscope or microscope system includes a step a) of implementing a workflow by one or more components of at least one microscope and/or microscope system, wherein the workflow comprises a capture of first data. In a step b), a trained model is determined for the workflow, at least in part based on the captured first data.

公开(公告)号：US20240273877A1

公开(公告)日：2024-08-15

申请号：US18437278

申请日：2024-02-09

Applicant: Leica Microsystems CMS GmbH

Inventor： Luciano Andre GUERREIRO-LUCAS ,  Constantin KAPPEL

IPC: G06V10/776 ,  G06T7/00 ,  G06V10/70 ,  G06V10/778 ,  G06V10/86 ,  G06V10/94 ,  G06V20/69

CPC classification number: G06V10/776 ,  G06T7/0012 ,  G06V10/778 ,  G06V10/86 ,  G06V10/87 ,  G06V10/945 ,  G06V20/69 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024

Abstract: Examples relate to method, system, and computer program for adjusting a first and a second machine-learning model, for processing a set of images, and to an imaging system. The method for adjusting a first and a second machine-learning model, comprises inputting a set of images representing a biological process into the first machine-learning model, it being trained to perform an image analysis workflow or to generate parameters for parametrizing an image analysis workflow. Then inputting an output of the image analysis workflow into the second machine-learning model, it being trained to output a prediction of a hypothesis being evaluated using the biological process. The method comprises calculating a loss function based on a difference between the prediction and an actual hypothesis being evaluated using the biological process. Then the first and/or second machine-learning model is adjusted based on the result of the loss function.

公开(公告)号：US20240203105A1

公开(公告)日：2024-06-20

申请号：US18539348

申请日：2023-12-14

Applicant: Leica Microsystems CMS GmbH

Inventor： Constantin KAPPEL

IPC: G06V10/776 ,  G06N3/0475 ,  G06N3/092 ,  G06V10/774 ,  G06V10/778 ,  G06V20/50

CPC classification number: G06V10/776 ,  G06N3/0475 ,  G06N3/092 ,  G06V10/774 ,  G06V10/7784 ,  G06V20/50 ,  G06V10/82 ,  G06V2201/03

Abstract: Examples relate to methods, systems, and computer systems for training a machine-learning model, for generating a training corpus, and for using a machine-learning model for use in a scientific or surgical imaging system, and to a scientific or surgical imaging system comprising such a system. A method for training a machine-learning model for use in a scientific or surgical imaging system comprises obtaining a plurality of images of a scientific or surgical imaging system, for use as training input images. The method comprises obtaining a plurality of training outputs that are based on the plurality of training input images and that are based on an image processing workflow of the scientific or surgical imaging system, the image processing workflow comprising a plurality of image processing steps. The method comprises training the machine-learning model using the plurality of training input images and the plurality of training outputs.

公开(公告)号：US20220245188A1

公开(公告)日：2022-08-04

申请号：US17596274

申请日：2019-06-07

Applicant: Leica Microsystems CMS GmbH

Inventor： Constantin KAPPEL

IPC: G06F16/53 ,  G06N3/08

Abstract: A system (100) for processing biology-related data comprises one or more processors (110) coupled to one or more storage devices (120). The system (100) is configured to receive biology-related image-based search data (103) and configured to generate a first high-dimensional representation of the biology-related image-based search data (103) by a trained visual recognition machine-learning algorithm executed by the one or more processors (110). The first high-dimensional representation comprises at least 3 entries each having a different value. Further, the system (100) is configured to obtain a plurality of second high-dimensional representations (105) of a plurality of biology-related image-based input data sets or of a plurality of biology-related language-based input data sets. Additionally, the system (100) is configured to compare the first high-dimensional representation with each second high-dimensional representation (105) of the plurality of second high-dimensional representations by the one or more processors (110).

公开(公告)号：US20240331417A1

公开(公告)日：2024-10-03

申请号：US18610349

申请日：2024-03-20

Applicant: Leica Microsystems CMS GmbH

Inventor： Constantin KAPPEL

IPC: G06V20/69 ,  G06V10/766 ,  G06V10/774 ,  G06V10/778

CPC classification number: G06V20/698 ,  G06V10/766 ,  G06V10/774 ,  G06V10/7792 ,  G06V20/693 ,  G06V20/695

Abstract: A method, system, and computer program for processing images of an optical imaging device and for training one or more machine-learning models. A method for processing images of an optical imaging device comprises obtaining embeddings of a plurality of candidate molecules, obtaining, for each candidate molecule, one or more images of the optical imaging device, the one or more images showing a visual representation of a target property exhibited by the candidate molecule in a biological sample, processing, using a machine-learning model, for each candidate molecule, the one or more images and/or information derived from the one or more images to generate a predicted embedding of the candidate molecule. The machine-learning model is trained to output the predicted embedding for an input comprising the one or more images and/or the information derived from the one or more images.