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公开(公告)号：US11436741B2
公开(公告)日：2022-09-06
申请号：US16315919
申请日：2018-03-06
Applicant: Google LLC
Inventor： Martin Stumpe , Varun Godbole
Abstract: A method for aligning two different magnified images of the same subject includes a first step of precomputing rigid transformations for the two different images globally (i.e., for all or approximately all regions of the images). Pairs of corresponding features in the two different magnified images are identified and transformation values are assigned to each of the pairs of corresponding features. In a second step, while images are being generated for display to a user, a locally optimal rigid transformation for the current field of view is computed. In a third step the images are aligned as per the locally optimal rigid transformation. Non-zero weight is given to transformation values for pairs of features that are outside the current field of view. Typically, the second and third steps are repeated many times as the images are generated for display and user either changes magnification or pans/navigates to a different location in the images.
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2.发明申请公开(公告)号：US20190340468A1
公开(公告)日：2019-11-07
申请号：US15972929
申请日：2018-05-07
Applicant: Google LLC
Inventor： Martin Stumpe , Timo Kohlberger
Abstract: A method is described for generating a prediction of a disease classification error for a magnified, digital microscope slide image of a tissue sample. The image is composed of a multitude of patches or tiles of pixel image data. An out-of-focus degree per patch is computed using a machine learning out-of-focus classifier. Data representing expected disease classifier error statistics of a machine learning disease classifier for a plurality of out-of-focus degrees is retrieved. A mapping of the expected disease classifier error statistics to each of the patches of the digital microscope slide image based on the computed out-of-focus degree per patch is computed, thereby generating a disease classifier error prediction for each of the patches. The disease classifier error predictions thus generated are aggregated over all of the patches.
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公开(公告)号：US11994664B2
公开(公告)日：2024-05-28
申请号：US17295353
申请日：2019-10-31
Applicant: GOOGLE LLC
Inventor： Jason Hipp , Martin Stumpe
CPC classification number: G02B21/32 , G02B21/365 , G06F3/167 , G06N20/00
Abstract: An augmented reality (AR) subsystem including one or more machine learning models, automatically overlays an augmented reality image, e.g., a border or outline, that identifies cells of potential interest, in the field of view of the specimen as seen through the eyepiece of an LCM microscope. The operator does not have to manually identify the cells of interest for subsequent LCM, e.g, on a workstation monitor, as in the prior art. The operator is provided with a switch, operator interface tool or other mechanism to select the identification of the cells, that is, indicate approval of the identification of the cells, while they view the specimen through the eyepiece. Activation of the switch or other mechanism invokes laser excising and capture of the cells of interest via a known and conventional LCM subsystem.
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公开(公告)号：US11783604B2
公开(公告)日：2023-10-10
申请号：US16959725
申请日：2018-01-11
Applicant: GOOGLE LLC
Inventor： Lily Peng , Martin Stumpe
CPC classification number: G06V20/695 , G01N1/30 , G01N1/34 , G06T7/0012 , G06T2207/20081 , G06T2207/20084 , G06T2207/30024
Abstract: A method for generating a ground truth mask for a microscope slide having a tissue specimen placed thereon includes a step of staining the tissue specimen with hematoxylin and eosin (H&E) staining agents. A first magnified image of the H&E stained tissue specimen is obtained, e.g., with a whole slide scanner. The H&E staining agents are then washed from the tissue specimen. A second, different stain is applied to the tissue specimen, e.g., a special stain such as an IHC stain. A second magnified image of the tissue specimen stained with the second, different stain is obtained. The first and second magnified images are then registered to each other. An annotation (e.g., drawing operation) is then performed on either the first or the second magnified images so as to form a ground truth mask, the ground truth mask in the form of closed polygon region enclosing tumor cells present in either the first or second magnified image.
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公开(公告)号：US11379516B2
公开(公告)日：2022-07-05
申请号：US16978102
申请日：2018-03-29
Applicant: GOOGLE LLC
Inventor： Lily Peng , Martin Stumpe , Daniel Smilkov , Jason Hipp
IPC: G06F16/532 , G06F16/55 , G06F16/583 , G06F16/538 , G06F16/2457 , G16H30/20 , G16H50/20 , G06K9/62 , G06V10/75
Abstract: A system for searching for similar medical images includes a reference library in the form of a multitude of medical images, at least some of which are associated with metadata including clinical information relating to the specimen or patient associated with the medical images. A computer system is configured as a search tool for receiving an input image query from a user. The computer system is trained to find one or more similar medical images in the reference library system which are similar to the input image. The reference library is represented as an embedding of each of the medical images projected in a feature space having a plurality of axes, wherein the embedding is characterized by two aspects of a similarity ranking: (1) visual similarity, and (2) semantic similarity such that neighboring images in the feature space are visually similar and semantic information is represented by the axes of the feature space. The computer system supports additional queries from a user to thereby further refine a search for medical images similar to the input image within a search space consisting of the one or more similar medical images.
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Patent Agency Ranking
公开(公告)号：US20210358140A1
公开(公告)日：2021-11-18
申请号：US16315919
申请日：2018-03-06
Applicant: Google LLC
Inventor： Martin Stumpe , Varun Godbole
Abstract: A method for aligning two different magnified images of the same subject includes a first step of precomputing rigid transformations for the two different images globally (i.e., for all or approximately all regions of the images). Pairs of corresponding features in the two different magnified images are identified and transformation values are assigned to each of the pairs of corresponding features. In a second step, while images are being generated for display to a user, a locally optimal rigid transformation for the current field of view is computed. In a third step the images are aligned as per the locally optimal rigid transformation. Non-zero weight is given to transformation values for pairs of features that are outside the current field of view. Typically, the second and third steps are repeated many times as the images are generated for display and user either changes magnification or pans/navigates to a different location in the images.
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7.发明授权公开(公告)号：US11657487B2
公开(公告)日：2023-05-23
申请号：US17493066
申请日：2021-10-04
Applicant: Google LLC
Inventor： Martin Stumpe , Timo Kohlberger
CPC classification number: G06T7/0002 , G06F18/217 , G06N3/08 , G06N20/00 , G06V20/693 , G06V20/698 , G16H30/40 , G06V2201/03
Abstract: A method is described for generating a prediction of a disease classification error for a magnified, digital microscope slide image of a tissue sample. The image is composed of a multitude of patches or tiles of pixel image data. An out-of-focus degree per patch is computed using a machine learning out-of-focus classifier. Data representing expected disease classifier error statistics of a machine learning disease classifier for a plurality of out-of-focus degrees is retrieved. A mapping of the expected disease classifier error statistics to each of the patches of the digital microscope slide image based on the computed out-of-focus degree per patch is computed, thereby generating a disease classifier error prediction for each of the patches. The disease classifier error predictions thus generated are aggregated over all of the patches.
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公开(公告)号：US20220019069A1
公开(公告)日：2022-01-20
申请号：US17295353
申请日：2019-10-31
Applicant: GOOGLE LLC
Inventor： Jason Hipp , Martin Stumpe
Abstract: An augmented reality (AR) subsystem including one or more machine learning models, automatically overlays an augmented reality image, e.g., a border or outline, that identifies cells of potential interest, in the field of view of the specimen as seen through the eyepiece of an LCM microscope. The operator does not have to manually identify the cells of interest for subsequent LCM, e.g, on a workstation monitor, as in the prior art. The operator is provided with a switch, operator interface tool or other mechanism to select the identification of the cells, that is, indicate approval of the identification of the cells, while they view the specimen through the eyepiece. Activation of the switch or other mechanism invokes laser excising and capture of the cells of interest via a known and conventional LCM subsystem.
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9.发明授权公开(公告)号：US11164048B2
公开(公告)日：2021-11-02
申请号：US16883014
申请日：2020-05-26
Applicant: Google LLC
Inventor： Martin Stumpe , Timo Kohlberger
Abstract: A method is described for generating a prediction of a disease classification error for a magnified, digital microscope slide image of a tissue sample. The image is composed of a multitude of patches or tiles of pixel image data. An out-of-focus degree per patch is computed using a machine learning out-of-focus classifier. Data representing expected disease classifier error statistics of a machine learning disease classifier for a plurality of out-of-focus degrees is retrieved. A mapping of the expected disease classifier error statistics to each of the patches of the digital microscope slide image based on the computed out-of-focus degree per patch is computed, thereby generating a disease classifier error prediction for each of the patches. The disease classifier error predictions thus generated are aggregated over all of the patches.
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10.发明申请公开(公告)号：US20220027678A1
公开(公告)日：2022-01-27
申请号：US17493066
申请日：2021-10-04
Applicant: Google LLC
Inventor： Martin Stumpe , Timo Kohlberger
Abstract: A method is described for generating a prediction of a disease classification error for a magnified, digital microscope slide image of a tissue sample. The image is composed of a multitude of patches or tiles of pixel image data. An out-of-focus degree per patch is computed using a machine learning out-of-focus classifier. Data representing expected disease classifier error statistics of a machine learning disease classifier for a plurality of out-of-focus degrees is retrieved. A mapping of the expected disease classifier error statistics to each of the patches of the digital microscope slide image based on the computed out-of-focus degree per patch is computed, thereby generating a disease classifier error prediction for each of the patches. The disease classifier error predictions thus generated are aggregated over all of the patches.
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