公开(公告)号：US12112475B2

公开(公告)日：2024-10-08

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 ,  G06V10/774 ,  G06V10/82 ,  G16H30/40 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  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：US20240331414A1

公开(公告)日：2024-10-03

申请号：US18218666

申请日：2023-07-06

Applicant: CooperSurgical, Inc.

Inventor： Charles Paradise ,  Michael Gerbush ,  Milan Ivosevic ,  Brian Costello ,  Paul DiCesare ,  Danial Ferreira ,  Ronald Green

IPC: G06V20/69 ,  G06T7/40 ,  G06V10/764 ,  G06V10/774 ,  G16B20/10 ,  G16H10/40

CPC classification number: G06V20/69 ,  G06T7/40 ,  G06V10/764 ,  G06V10/774 ,  G16B20/10 ,  G16H10/40 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06V2201/03

Abstract: The present disclosure relates to a method performed by one or more computers for tracking a biological material of a subject during an in-vitro fertilization process. The method includes receiving, from a camera, an image of a dish having a visual characteristic and a drop disposed on the dish, the dish holding the biological material at a drop location. The method then includes processing the image of the dish, using a drop identification model, to identify the drop according to the visual characteristic. Further, the method includes assigning an identifier to the drop associated with the drop location, and recording the identifier of the drop associated with the drop location.

公开(公告)号：US20240331337A1

公开(公告)日：2024-10-03

申请号：US18622855

申请日：2024-03-29

Applicant: SHANGHAI UNITED IMAGING HEALTHCARE CO., LTD.

Inventor： Yang LYU ,  Houjiao DAI ,  Chen XI

IPC: G06V10/25 ,  G06V10/80 ,  G06V10/82

CPC classification number: G06V10/25 ,  G06V10/80 ,  G06V10/82 ,  G06V2201/03

Abstract: Embodiments of the present disclosure provide a medical image processing method, system, and storage medium. The method includes determining a plurality of images of regions of interest (ROIs) based on a first image of a subject; determining a plurality of second images based on the plurality of images of the ROIs and image processing models corresponding to the plurality of images of the ROIs; and performing a fusion operation based on the plurality of second images to obtain a target image.

公开(公告)号：US12094193B2

公开(公告)日：2024-09-17

申请号：US17463898

申请日：2021-09-01

Applicant: BOE Technology Group Co., Ltd.

Inventor： Yingying Li ,  Zhenglong Li

IPC: G06V10/82 ,  G06F18/2413 ,  G06N3/02 ,  G06T7/00 ,  G06T7/11 ,  G06V10/40 ,  G06V10/764 ,  G06V10/44

CPC classification number: G06V10/82 ,  G06F18/2413 ,  G06N3/02 ,  G06T7/0002 ,  G06T7/11 ,  G06V10/40 ,  G06V10/764 ,  G06F2218/08 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06V10/454 ,  G06V2201/03

Abstract: The embodiments of the present disclosure disclose an image processing method and device. The image processing method comprises transforming a first image to obtain a plurality of second images; obtaining feature maps of each of the second images by performing feature extraction on the second images using a first machine learning unit selected from a group including at least one first machine learning unit; and inputting the feature maps of each of the second images to a second machine learning unit to obtain a processing result of the first image.

公开(公告)号：US12089977B2

公开(公告)日：2024-09-17

申请号：US17317746

申请日：2021-05-11

Applicant: Pie Medical Imaging B.V.

Inventor： Ivana Isgum ,  Majd Zreik ,  Tim Leiner ,  Jean-Paul Aben

IPC: A61B6/50 ,  G06F18/2411 ,  G06N3/088 ,  G06T7/00 ,  G06T7/10 ,  G06V10/44 ,  G06V10/762 ,  G06V10/764 ,  G06V20/64 ,  G16H30/40 ,  G16H50/50

CPC classification number: A61B6/507 ,  G06F18/2411 ,  G06N3/088 ,  G06T7/0012 ,  G06T7/10 ,  G06V10/443 ,  G06V10/763 ,  G06V10/764 ,  G06V20/653 ,  G16H30/40 ,  G16H50/50 ,  G06T2207/20081 ,  G06T2207/30104 ,  G06V2201/03

Abstract: Methods and systems are provided for assessing the presence of functionally significant stenosis in one or more coronary arteries, further known as a severity of vessel obstruction. The methods and systems can implement a prediction phase that comprises segmenting at least a portion of a contrast enhanced volume image data set into data segments corresponding to wall regions of the target organ, and analyzing the data segments to extract features that are indicative of an amount of perfusion experiences by wall regions of the target organ. The methods and systems can obtain a feature-perfusion classification (FPC) model derived from a training set of perfused organs, classify the data segments based on the features extracted and based on the FPC model, and provide, as an output, a prediction indicative of a severity of vessel obstruction based on the classification of the features.

公开(公告)号：US12089976B2

公开(公告)日：2024-09-17

申请号：US17233524

申请日：2021-04-18

Applicant: FUJIFILM Corporation

Inventor： Futoshi Sakuragi

IPC: A61B6/46 ,  A61B6/03 ,  G06T11/00 ,  G06V10/25 ,  G06V10/44 ,  G06V10/98

CPC classification number: A61B6/469 ,  A61B6/032 ,  A61B6/463 ,  G06T11/008 ,  G06V10/25 ,  G06V10/44 ,  G06V10/98 ,  G06V2201/03

Abstract: The display controller displays a first tomographic image of a three-dimensional image consisting of a plurality of tomographic images on a display unit. A first correction unit corrects the boundary of a first region of interest extracted from the first tomographic image, by a correction instruction using a correction instruction region for the boundary of the first region of interest. The first instruction region setting unit sets a first instruction region on a second tomographic image of the plurality of tomographic images. The second correction unit corrects the boundary of a second region of interest extracted from the second tomographic image by setting a second instruction region on the second tomographic image.

公开(公告)号：US20240303811A1

公开(公告)日：2024-09-12

申请号：US18598539

申请日：2024-03-07

Applicant: SCREEN Holdings Co., Ltd.

Inventor： Ryo HASEBE

IPC: G06T7/00 ,  G06V10/44

CPC classification number: G06T7/0012 ,  G06V10/44 ,  G06T2207/10101 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06V2201/03

Abstract: First, a biological sample is imaged, and a photographic image in which intensity values are distributed is acquired. After that, a localization region corresponding to an unusual part is extracted from the photographic image. At that time, a region of which intensity value satisfies a predetermined requirement in the photographic image is extracted as the localization region. Alternatively, the photographic image is input to a trained model created in advance, and a localization region output from the trained model is obtained. In this manner, the localization region corresponding to the unusual part can be extracted from the photographic image of the biological sample. This enables noninvasive observation of the unusual part of the biological sample without processing a cell by staining or the like.

公开(公告)号：US12078703B2

公开(公告)日：2024-09-03

申请号：US17610455

申请日：2020-05-16

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Irina Fudulova ,  Fedor Mushenok

IPC: G06V10/25 ,  A61B5/00 ,  A61B5/055 ,  G01R33/54 ,  G01R33/56 ,  G06F18/214 ,  G06V10/82

CPC classification number: G01R33/543 ,  A61B5/0037 ,  A61B5/055 ,  A61B5/7235 ,  A61B5/7425 ,  G01R33/5608 ,  G06F18/214 ,  G06V10/25 ,  G06V10/82 ,  G06V2201/03 ,  G06V2201/10

Abstract: Disclosed herein is a medical system (100, 300, 500) comprising a memory (110) storing machine executable instructions (120) and a predictor algorithm (122) configured for outputting predicted field of view alignment data (128) for a magnetic resonance imaging system (502) in response to inputting one or more localizer magnetic resonance images (124) and subject metadata (126). The predictor algorithm comprises a trainable machine learning algorithm. The medical system further comprises a processor (104) configured for controlling the medical system. Execution of the machine executable instructions causes the processor to: receive (200) the one or more localizer magnetic resonance images and the subject metadata; and receive (202) the predicted field of view alignment data from the predictor algorithm in response to inputting the one or more localizer magnetic resonance images into the predictor algorithm and in response to inputting the subject metadata.

公开(公告)号：US20240290076A1

公开(公告)日：2024-08-29

申请号：US18528675

申请日：2023-12-04

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Mohammad Reza Hosseinzadeh Taher ,  Jianming Liang

IPC: G06V10/774 ,  G06V10/82 ,  G06V20/50 ,  G06V40/10 ,  G16H30/40

CPC classification number: G06V10/774 ,  G06V10/82 ,  G06V20/50 ,  G06V40/10 ,  G16H30/40 ,  G06V2201/03

Abstract: Systems, methods, and apparatuses for learning foundation models from anatomy in medical imaging for use with medical image classification and/or image segmentation in the context of medical image analysis. Exemplary systems include means for receiving medical images; extracting human anatomical patterns from the medical images; generating a foundation model via learning the human anatomical patterns from within the medical images received, resulting in generic representations of the human anatomical patterns; wherein the learning includes: first learning prominent objects from within the medical images received corresponding to the human anatomical patterns; and secondly learning detailed parts within the learned prominent objects corresponding to sub-portions of the generic representations of the human anatomical patterns; wherein the learning further includes executing a self-supervised contrastive learning framework, including: executing an anatomy decomposer (AD) of the self-supervised contrastive learning framework which guides the generated foundation model to conserve hierarchical relationships of anatomical structures within the medical images received; and executing a purposive pruner (PP) of the self-supervised contrastive learning framework which forces the model to capture more distinct representations for different anatomical structures at varying granularity levels; and outputting the generated foundation model for use in processing medical images which form no part of the medical images received and used for training the generated foundation model.

公开(公告)号：US20240281980A1

公开(公告)日：2024-08-22

申请号：US18651014

申请日：2024-04-30

Applicant: LightLab Imaging, Inc.

Inventor： Shimin Li ,  Ajay Gopinath ,  Kyle Savidge

IPC: G06T7/11 ,  G06F18/211 ,  G06F18/214 ,  G06F18/40 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06T7/11 ,  G06F18/211 ,  G06F18/214 ,  G06F18/40 ,  G06N3/04 ,  G06N3/08 ,  G06V2201/03

Abstract: In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.