公开(公告)号：US20230186658A1

公开(公告)日：2023-06-15

申请号：US17996799

申请日：2021-04-20

Applicant: SONY GROUP CORPORATION

Inventor： JUNICHIRO ENOKI ,  KENJI YAMANE

IPC: G06V20/69 ,  G06V10/22 ,  G16H30/40

CPC classification number: G06V20/695 ,  G06V10/22 ,  G16H30/40 ,  G06V2201/03

Abstract: A generation device includes: a generation section that, on the basis of feature values of partial regions set in an image regarding pathology, generates distribution information in which distributions of the partial regions on a feature space specified by feature values included in the partial regions are visibly arranged in the feature space while being associated with the partial regions.

公开(公告)号：US11670143B2

公开(公告)日：2023-06-06

申请号：US17175897

申请日：2021-02-15

Applicant: Gauss Surgical, Inc.

Inventor： Siddarth Satish ,  Andrew T. Hosford ,  Kevin J. Miller ,  Milton McColl ,  Juan Carlos Aragon

IPC: G06T7/62 ,  G08B21/02 ,  H04W4/021 ,  H04W4/80 ,  H04W4/029 ,  G08B13/14 ,  H04W4/02 ,  H04W4/20 ,  A61B5/02 ,  H04W8/00 ,  G06V10/25 ,  G06V40/16 ,  G06V10/24 ,  G06F3/0488 ,  G06K7/00 ,  G06K7/08 ,  G06K7/10 ,  G06K19/06 ,  G06K19/07 ,  G06K19/073 ,  G06K19/077 ,  G06K19/08 ,  G06Q20/18 ,  G06Q20/20 ,  G06Q20/34 ,  G06Q20/38 ,  G06Q20/40 ,  G06Q30/0207 ,  G06Q30/0241 ,  G06Q30/0601 ,  G07F7/08 ,  G07F7/10 ,  G08B25/01 ,  G08B5/22 ,  H04W84/18

CPC classification number: G08B21/0272 ,  A61B5/02 ,  A61B5/02042 ,  G06F3/0488 ,  G06K7/0004 ,  G06K7/084 ,  G06K7/087 ,  G06K7/10297 ,  G06K19/06187 ,  G06K19/06206 ,  G06K19/07 ,  G06K19/0702 ,  G06K19/0704 ,  G06K19/0723 ,  G06K19/0725 ,  G06K19/0775 ,  G06K19/07345 ,  G06K19/07703 ,  G06K19/07705 ,  G06K19/07707 ,  G06K19/07709 ,  G06K19/07749 ,  G06K19/07766 ,  G06K19/07769 ,  G06K19/07773 ,  G06K19/083 ,  G06Q20/18 ,  G06Q20/20 ,  G06Q20/34 ,  G06Q20/341 ,  G06Q20/3415 ,  G06Q20/352 ,  G06Q20/385 ,  G06Q20/401 ,  G06Q30/0222 ,  G06Q30/0241 ,  G06Q30/0277 ,  G06Q30/0641 ,  G06T7/62 ,  G06V10/24 ,  G06V10/25 ,  G06V40/172 ,  G07F7/0806 ,  G07F7/1008 ,  G08B13/1427 ,  G08B21/0244 ,  G08B21/0247 ,  G08B21/0269 ,  G08B21/0277 ,  H04W4/02 ,  H04W4/021 ,  H04W4/029 ,  H04W4/20 ,  H04W4/80 ,  H04W8/005 ,  G06T2207/10024 ,  G06T2207/30004 ,  G06V2201/03 ,  G08B5/22 ,  G08B25/016 ,  H04W84/18

Abstract: A method and system for communicating estimated blood loss parameters of a patient to a user, the method comprising: receiving data representative of an image, of a fluid receiver; automatically detecting a region within the image associated with a volume of fluid received at the fluid receiver, the volume of fluid including a blood component; calculating an estimated amount of the blood component present in the volume of fluid based upon a color parameter represented in the region, and determining a bias error associated with the estimated amount of the blood component; updating an analysis of an aggregate amount of the blood component and an aggregate bias error associated with blood loss of the patient, based upon the estimated amount of the blood component and the bias error; and providing information from the analysis of the aggregate amount of the blood component and the aggregate bias error, to the user.

公开(公告)号：US20230153994A1

公开(公告)日：2023-05-18

申请号：US17659914

申请日：2022-04-20

Applicant: Wuhan University

Inventor： Juan Liu ,  Yuqi Chen ,  Jing Feng

IPC: G06T7/00 ,  G06T7/11 ,  G06V10/764 ,  G06V10/82 ,  G06V10/774 ,  G16H50/20 ,  G16H30/40

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06V10/82 ,  G06V10/764 ,  G06V10/774 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/10056 ,  G06T2207/20036 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06T2207/30068 ,  G06T2207/30096 ,  G06V2201/03

Abstract: Provided is a method and system for predicting tumor mutation burden (TMB) in triple negative breast cancer (TNBC) based on nuclear scores and histopathological whole slide images (WSIs). The method includes the following steps: first, screening the histopathological WSIs of TNBC; calculating a TMB value of each patient according to gene mutation of each patient with TNBC, and dividing the TMB values into two groups with high and low TMB according to a set threshold; dividing the histopathological WSIs of TNBC into patches of a set size; screening a certain number of patches with high nuclear scores according to a nuclear score function; then building a convolutional neural network (CNN) classification model, and stochastically initializing parameters in the CNN classification model; and finally, putting the screened patches into the built CNN classification model for training, so as to automatically predict high or low TMB with the histopathological WSIs of TNBC.

公开(公告)号：US11645531B2

公开(公告)日：2023-05-09

申请号：US17117756

申请日：2020-12-10

Applicant: HOWMEDICA OSTEONICS CORP.

Inventor： Jesse G. Moore ,  Sergii Poltaretskyi ,  Jean Chaoui ,  Damien Cariou

IPC: A61B34/00 ,  A61B34/10 ,  A61B90/00 ,  G02B27/01 ,  G06F3/0482 ,  G06T11/00 ,  A61B5/11 ,  A61B5/00 ,  G06T19/00 ,  G16H30/40 ,  G16H40/60 ,  H04N13/332 ,  H04N13/122 ,  A61B17/16 ,  A61B17/17 ,  G02B27/00 ,  G16H20/40 ,  G16H50/50 ,  G16H70/20 ,  G09B9/00 ,  G09B19/00 ,  G09B23/28 ,  G06N20/00 ,  G06F3/01 ,  A61B17/14 ,  G06T7/55 ,  A61F2/40 ,  A61B90/92 ,  G16H40/63 ,  G09B5/06 ,  G16H40/67 ,  G06T19/20 ,  G16H40/20 ,  G16H30/20 ,  G06T7/11 ,  G16H50/30 ,  G06N3/08 ,  G06T7/00 ,  G16H70/60 ,  G16H50/70 ,  G06F30/10 ,  G06K9/62 ,  G16H80/00 ,  G06F3/04815 ,  G16H50/20 ,  A61B17/00 ,  A61F2/46 ,  A61B17/15 ,  G06F3/0483 ,  A61B34/20 ,  G06N3/04

CPC classification number: A61B34/25 ,  A61B5/1114 ,  A61B5/1121 ,  A61B5/1127 ,  A61B5/681 ,  A61B17/142 ,  A61B17/1604 ,  A61B17/1626 ,  A61B17/1659 ,  A61B17/1684 ,  A61B17/1703 ,  A61B34/10 ,  A61B34/76 ,  A61B90/08 ,  A61B90/36 ,  A61B90/361 ,  A61B90/37 ,  A61B90/39 ,  A61B90/92 ,  A61F2/40 ,  A61F2/4081 ,  G02B27/0075 ,  G02B27/017 ,  G02B27/0172 ,  G06F3/011 ,  G06F3/0482 ,  G06F3/04815 ,  G06F30/10 ,  G06K9/6261 ,  G06N3/08 ,  G06N20/00 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/55 ,  G06T11/00 ,  G06T19/006 ,  G06T19/20 ,  G09B5/06 ,  G09B9/00 ,  G09B19/003 ,  G09B23/28 ,  G16H20/40 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H40/60 ,  G16H40/63 ,  G16H40/67 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/20 ,  G16H70/60 ,  G16H80/00 ,  H04N13/122 ,  H04N13/332 ,  A61B5/744 ,  A61B17/151 ,  A61B17/1775 ,  A61B17/1778 ,  A61B2017/00115 ,  A61B2017/00119 ,  A61B2034/102 ,  A61B2034/104 ,  A61B2034/105 ,  A61B2034/107 ,  A61B2034/108 ,  A61B2034/2051 ,  A61B2034/2055 ,  A61B2034/2065 ,  A61B2034/2068 ,  A61B2034/252 ,  A61B2034/254 ,  A61B2090/062 ,  A61B2090/0807 ,  A61B2090/365 ,  A61B2090/366 ,  A61B2090/367 ,  A61B2090/373 ,  A61B2090/374 ,  A61B2090/378 ,  A61B2090/3762 ,  A61B2090/397 ,  A61B2090/3937 ,  A61B2090/3945 ,  A61B2505/05 ,  A61B2562/0219 ,  A61F2/4606 ,  A61F2/4612 ,  A61F2002/4011 ,  A61F2002/4633 ,  A61F2002/4658 ,  A61F2002/4668 ,  G02B2027/0141 ,  G02B2027/0174 ,  G06F3/0483 ,  G06N3/04 ,  G06T2200/24 ,  G06T2207/10016 ,  G06T2207/20036 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30008 ,  G06T2207/30052 ,  G06T2207/30204 ,  G06T2210/41 ,  G06T2219/2004 ,  G06V2201/03 ,  G16H50/20

Abstract: An example method includes obtaining, a virtual model of a portion of an anatomy of a patient obtained from a virtual surgical plan for an orthopedic joint repair surgical procedure to attach a prosthetic to the anatomy; identifying, based on data obtained by one or more sensors, positions of one or more physical markers positioned relative to the anatomy of the patient; and registering, based on the identified positions, the virtual model of the portion of the anatomy with a corresponding observed portion of the anatomy.

公开(公告)号：US20240362847A1

公开(公告)日：2024-10-31

申请号：US18291268

申请日：2022-07-15

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Frank Michael Weber ,  Alexandra Groth ,  Harald Greiner ,  Jonathan Thomas Sutton ,  Balasundar Raju ,  Shyam Bharat ,  Peter Bingley

IPC: G06T15/00 ,  G06T17/00 ,  G06T19/20 ,  G06V20/64

CPC classification number: G06T15/00 ,  G06T17/00 ,  G06T19/20 ,  G06V20/64 ,  G06T2200/24 ,  G06T2210/41 ,  G06T2219/2016 ,  G06V2201/03

Abstract: According to an aspect, there is provided a computer-implemented method of operating a visual data delivery system. The method comprises: processing (901) a sequence of 3-dimensional, 3D, images of a body to generate first 2-dimensional, 2D, image data representing a first sequence of 2D images of the body, wherein the 2D images are images of the body in a 2D image plane through the 3D images, and wherein an amount of data representing the first 2D image data is less than an amount of data representing the 3D images from which the first 2D image data is generated; sending (903) the first 2D image data to a display system for display of the first sequence of 2D images of the body by the display system; receiving (905) a 2D image plane adjustment indication from the display system, wherein the 2D image plane adjustment indication indicates a required rotation and/or translation of the 2D image plane; processing (907) the sequence of 3D images and/or a further sequence of 3D images to generate second 2D image data representing a second sequence of 2D images of the body, wherein the 2D images in the second sequence of 2D images are images of the body in the rotated and/or translated 2D image plane; and sending (909) the second 2D image data to the display system for display of the second sequence of 2D images of the body by the display system.

公开(公告)号：US20240362781A1

公开(公告)日：2024-10-31

申请号：US18767169

申请日：2024-07-09

Applicant: ADIUVO DIAGNOSTICS PRIVATE LIMITED

Inventor： Bala Pesala ,  Geethanjali Radhakrishnan ,  Bikki Kumar Sha ,  John King

IPC: G06T7/00 ,  G01N21/64 ,  G06T7/50 ,  G06V10/10 ,  G06V10/60 ,  G06V10/82

CPC classification number: G06T7/0012 ,  G06T7/50 ,  G06V10/17 ,  G06V10/60 ,  G06V10/82 ,  G01N21/6486 ,  G06T2207/10064 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06T2207/30088 ,  G06V2201/03 ,  G06V2201/07

Abstract: Techniques are for detecting presence of a problematic cellular entity in a target. In an example, using an analysis model, a fluorescence-based image is analyzed. The analysis model is trained using a number of reference fluorescence-based images for detecting the presence of problematic cellular entities in targets. Based on the analysis, a problematic cellular entity present in the target is detected. To perform the detection, the analysis model is trained to differentiate between the fluorescence in the fluorescence-based image emerging from the problematic cellular entity and the fluorescence in the fluorescence-based image emerging from regions other than the problematic cellular entity.

公开(公告)号：US20240355094A1

公开(公告)日：2024-10-24

申请号：US18683595

申请日：2022-08-11

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Sergey Kastryulin ,  Alexey Chernyavskiy ,  Nicola Pezzotti

IPC: G06V10/77 ,  G06T11/00 ,  G06V10/22 ,  G06V10/46 ,  G06V10/774 ,  G06V10/98

CPC classification number: G06V10/7715 ,  G06T11/005 ,  G06V10/235 ,  G06V10/464 ,  G06V10/774 ,  G06V10/993 ,  G06V2201/03

Abstract: Disclosed herein is a medical system (100) comprising a memory (110) storing machine executable instructions (120). The memory (110) further stores a trained first machine learning module (122) trained to output in response to receiving a medical image (124) as input a saliency map (126) as output. The saliency map (126) is predictive of a distribution of user attention over the medical image (124). The medical system (100) further comprises a computational system (104). Execution of the machine executable instructions (120) causes the computational system (104) to receive a medical image (124). The medical image (124) is provided as input to the trained first machine learning module (122). In response to the providing of the medical image (124), a saliency map (126) of the medical image (124) is received as output from the trained first machine learning module (122). The saliency map (126) predicts a distribution of user attention over the medical image (124). The saliency map (126) of the medical image (124) is provided.

公开(公告)号：US12125577B2

公开(公告)日：2024-10-22

申请号：US17117860

申请日：2020-12-10

Applicant: HOWMEDICA OSTEONICS CORP.

Inventor： W. Matthew Kuester ,  Sergii Poltaretskyi ,  Jean Chaoui ,  Damien Cariou

IPC: G16H20/40 ,  A61B5/00 ,  A61B5/11 ,  A61B17/14 ,  A61B17/16 ,  A61B17/17 ,  A61B34/00 ,  A61B34/10 ,  A61B90/00 ,  A61B90/92 ,  A61F2/40 ,  G02B27/00 ,  G02B27/01 ,  G06F3/01 ,  G06F3/04815 ,  G06F3/0482 ,  G06F30/10 ,  G06N3/08 ,  G06N20/00 ,  G06T7/00 ,  G06T7/11 ,  G06T7/55 ,  G06T11/00 ,  G06T19/00 ,  G06T19/20 ,  G09B5/06 ,  G09B9/00 ,  G09B19/00 ,  G09B23/28 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H40/60 ,  G16H40/63 ,  G16H40/67 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/20 ,  G16H70/60 ,  G16H80/00 ,  H04N13/122 ,  H04N13/332 ,  A61B17/00 ,  A61B17/15 ,  A61B34/20 ,  A61B90/50 ,  A61F2/46 ,  G06F3/0483 ,  G06N3/04 ,  G16H50/20

CPC classification number: G16H20/40 ,  A61B5/1114 ,  A61B5/1121 ,  A61B5/1127 ,  A61B5/681 ,  A61B17/142 ,  A61B17/1604 ,  A61B17/1626 ,  A61B17/1659 ,  A61B17/1684 ,  A61B17/1703 ,  A61B34/10 ,  A61B34/25 ,  A61B90/08 ,  A61B90/36 ,  A61B90/361 ,  A61B90/37 ,  A61B90/39 ,  A61B90/92 ,  A61F2/40 ,  A61F2/4081 ,  G02B27/0075 ,  G02B27/017 ,  G02B27/0172 ,  G06F3/011 ,  G06F3/04815 ,  G06F3/0482 ,  G06F30/10 ,  G06N3/08 ,  G06N20/00 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/55 ,  G06T11/00 ,  G06T19/006 ,  G06T19/20 ,  G09B5/06 ,  G09B9/00 ,  G09B19/003 ,  G09B23/28 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H40/60 ,  G16H40/63 ,  G16H40/67 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/20 ,  G16H70/60 ,  G16H80/00 ,  H04N13/122 ,  H04N13/332 ,  A61B5/744 ,  A61B2017/00115 ,  A61B2017/00119 ,  A61B2017/00123 ,  A61B17/151 ,  A61B17/1775 ,  A61B17/1778 ,  A61B2034/102 ,  A61B2034/104 ,  A61B2034/105 ,  A61B2034/107 ,  A61B2034/108 ,  A61B2034/2048 ,  A61B2034/2051 ,  A61B2034/2055 ,  A61B2034/2065 ,  A61B2034/2068 ,  A61B2034/252 ,  A61B2034/254 ,  A61B2090/062 ,  A61B2090/067 ,  A61B2090/0801 ,  A61B2090/0807 ,  A61B2090/365 ,  A61B2090/366 ,  A61B2090/367 ,  A61B2090/368 ,  A61B2090/373 ,  A61B2090/374 ,  A61B2090/3762 ,  A61B2090/378 ,  A61B2090/3937 ,  A61B2090/3945 ,  A61B2090/397 ,  A61B2090/502 ,  A61B2505/05 ,  A61B2562/0219 ,  A61F2002/4011 ,  A61F2/4606 ,  A61F2/4612 ,  A61F2002/4633 ,  A61F2002/4658 ,  A61F2002/4668 ,  G02B2027/0141 ,  G02B2027/0174 ,  G06F3/0483 ,  G06N3/04 ,  G06T2200/24 ,  G06T2207/10016 ,  G06T2207/20036 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30008 ,  G06T2207/30052 ,  G06T2207/30204 ,  G06T2210/41 ,  G06T2219/2004 ,  G06V2201/03 ,  G16H50/20

Abstract: An example system for demonstrating at least one aspect of an orthopedic surgical procedure includes a first device and a second device. In this example, the first device is configured to display a presentation to a first user, wherein the presentation includes one or more virtual elements and wherein the one or more virtual elements comprise a three-dimensional (3D) virtual representation of one or more anatomical features associated with the orthopedic surgical procedure. In this example, the second device is configured to display the presentation to a second user. In this example, the one or more virtual elements demonstrate at least one aspect of the orthopedic surgical procedure and wherein control of at least some of the one or more virtual elements are assignable from the first device to the second device.

公开(公告)号：US12125203B2

公开(公告)日：2024-10-22

申请号：US17780017

申请日：2020-10-14

Applicant: Carl Zeiss Meditec AG

Inventor： Patrick Hoyer ,  Stefan Saur ,  Gerald Panitz

IPC: G06T7/00 ,  G06T7/30 ,  G06V20/69

CPC classification number: G06T7/0012 ,  G06T7/30 ,  G06V20/698 ,  G06T2207/10016 ,  G06T2207/10056 ,  G06T2207/10068 ,  G06T2207/20016 ,  G06T2207/30004 ,  G06V2201/03

Abstract: The invention relates to a method for determining information for distinguishing between tissue fluid cells and tissue cells in a high-resolution image of a tissue area. In the method, images stored temporarily with a low resolution and a high image rate before the high-resolution image is recorded are accessed and the information for distinguishing between tissue fluid cells and tissue cells is obtained from the temporarily stored images with the low resolution and the high image rate.

公开(公告)号：US20240346838A1

公开(公告)日：2024-10-17

申请号：US18510513

申请日：2023-11-15

Applicant: HistoWiz, Inc.

Inventor： Ke CHENG ,  Matthew WILDER ,  Weijian LI

IPC: G06V20/69 ,  G06F16/532 ,  G06V10/70 ,  G06V10/77 ,  G06V10/82 ,  G06V20/70

CPC classification number: G06V20/698 ,  G06F16/532 ,  G06V10/70 ,  G06V10/7715 ,  G06V10/82 ,  G06V20/695 ,  G06V20/70 ,  G06V2201/03

Abstract: Provided herein are methods and systems for performing an automated tagging of features in digital micrographs representing slides with tissue samples. Automated tagging of features may include automated entry of metadata associated with whole slide images or regions of the whole slide images.