公开(公告)号：US11721023B1

公开(公告)日：2023-08-08

申请号：US17967696

申请日：2022-10-17

Applicant: HeHealth PTE Ltd.

Inventor： Sembukuttige Yudara Madusanka Kularathne ,  Mathugamavithanage Dilruk Dasantha Perera

IPC: G06T7/00 ,  G06T7/194 ,  G06T5/20 ,  G06V10/764 ,  G06V10/774 ,  G06V20/70 ,  G16H30/40 ,  G16H50/20 ,  G06T5/00 ,  A61B5/00

CPC classification number: G06T7/0014 ,  A61B5/0077 ,  A61B5/43 ,  G06T5/002 ,  G06T5/20 ,  G06T7/194 ,  G06V10/764 ,  G06V10/774 ,  G06V20/70 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20132 ,  G06T2207/20212 ,  G06T2207/30004 ,  G06V2201/03

Abstract: Disclosed herein are system, method, and computer program product embodiments for distinguishing a disease state from a non-disease state in an image. An embodiment operates by receiving an image of a target area over a network. The embodiment then corrects for background noise in the image by applying a semantic segmentation filter to obtain a segmented image. The sematic segmentation filter may be trained to remove the background noise from the image. The embodiment then determines, using a trained artificial intelligence (AI) model and the segmented image, at least one classification for the target area. The embodiment finally causes the display of the at least one classification and disease information on a user device associated with a user. The trained AI model may be trained using at least augmented images obtained from a set of images to correct for at least an imbalance in the set of images.

公开(公告)号：US11720647B2

公开(公告)日：2023-08-08

申请号：US16999665

申请日：2020-08-21

Applicant: GE Precision Healthcare LLC

Inventor： Ravi Soni ,  Tao Tan ,  Gopal B. Avinash ,  Dibyajyoti Pati ,  Hans Krupakar ,  Venkata Ratnam Saripalli

IPC: G06F18/214 ,  G06N20/00 ,  G06F18/21 ,  G06N3/08 ,  G06N20/10 ,  G06V10/774 ,  G16H30/40 ,  G06T11/00

CPC classification number: G06F18/2148 ,  G06F18/214 ,  G06F18/2163 ,  G06N3/08 ,  G06N20/00 ,  G06N20/10 ,  G06T11/00 ,  G06V10/774 ,  G16H30/40 ,  G06V2201/03

Abstract: Systems and techniques that facilitate synthetic training data generation for improved machine learning generalizability are provided. In various embodiments, an element augmentation component can generate a set of preliminary annotated training images based on an annotated source image. In various aspects, a preliminary annotated training image can be formed by inserting at least one element of interest or at least one background element into the annotated source image. In various instances, a modality augmentation component can generate a set of intermediate annotated training images based on the set of preliminary annotated training images. In various cases, an intermediate annotated training image can be formed by varying at least one modality-based characteristic of a preliminary annotated training image. In various aspects, a geometry augmentation component can generate a set of deployable annotated training images based on the set of intermediate annotated training images. In various instances, a deployable annotated training image can be formed by varying at least one geometric characteristic of an intermediate annotated training image. In various embodiments, a training component can train a machine learning model on the set of deployable annotated training images.

公开(公告)号：US11717243B2

公开(公告)日：2023-08-08

申请号：US17064246

申请日：2020-10-06

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Takeshi Ishii

IPC: A61B6/00 ,  G06T7/50 ,  G06T7/70 ,  G06T19/20 ,  G06V20/00 ,  G06V20/64

CPC classification number: A61B6/4441 ,  A61B6/547 ,  G06T7/50 ,  G06T7/70 ,  G06T19/20 ,  G06V20/00 ,  G06V20/64 ,  G06T2207/20081 ,  G06T2210/41 ,  G06T2219/2016 ,  G06V2201/03

Abstract: A medical information processing apparatus according to an embodiment includes a processor. The processor acquires an examination room image obtained by capturing the inside of an examination room in which a medical imaging apparatus including a movement mechanism is installed. The processor specifies identification information of a target object depicted in the examination room image using the examination room image. The processor specifies three-dimensional shape data corresponding to the identification information of the target object using the identification information of the target object. The processor specifies the position of the three-dimensional shape data in a virtual three-dimensional space using depth information indicating distance from an examination-room image capturing device to the target object depicted in the examination room image.

公开(公告)号：US20230245784A1

公开(公告)日：2023-08-03

申请号：US18131859

申请日：2023-04-06

Applicant: Axial Medical Printing Limited

Inventor： Daniel CRAWFORD ,  Rory HANRATTY ,  Luke DONNELLY ,  Luis TRINDADE ,  Thomas SCHWARZ ,  Adam HARPUR

IPC: G16H50/50 ,  G06T7/62 ,  G16H30/40 ,  G06V20/70 ,  G06V10/26 ,  G06V10/764 ,  G06T7/00 ,  G06T15/04 ,  G06T15/06 ,  G06T17/20

CPC classification number: G16H50/50 ,  G06T7/62 ,  G16H30/40 ,  G06V20/70 ,  G06V10/26 ,  G06V10/764 ,  G06T7/0016 ,  G06T15/04 ,  G06T15/06 ,  G06T17/20 ,  G06V2201/03 ,  G06T2210/21 ,  G06T2210/41

Abstract: Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical feature identification algorithm to identify one or more patient specific anatomical features within the medical images by exploring an anatomical knowledge dataset. A 3D surface mesh model may be generated representing the one or more classified patient specific anatomical features, such that information may be extracted from the 3D surface mesh model based on the selected pathology. Physiological information associated with the selected pathology for the 3D surface mesh model may be generated based on the extracted information.

公开(公告)号：US20230245497A1

公开(公告)日：2023-08-03

申请号：US18007790

申请日：2020-06-05

Applicant: NEC Corporation

Inventor： Ryoma Oami

IPC: G06V40/18 ,  G06T7/33

CPC classification number: G06V40/193 ,  G06V40/197 ,  G06T7/337 ,  G06V2201/03

Abstract: An information processing device detects iris images of cosmetic lenses. A candidate extraction means extracts different iris images corresponding to the same person to be a cosmetic lens candidate. A confidence level calculation means matches iris features of the cosmetic lens candidate with iris features of other cosmetic lenses, and calculates a confidence level indicating a cosmetic lens likelihood. A determination means determines that the cosmetic lens candidate is a cosmetic lens with respect to the calculated confidence level that is equal to or higher than a predetermined threshold value.

公开(公告)号：US20230245309A1

公开(公告)日：2023-08-03

申请号：US18295577

申请日：2023-04-04

Applicant: PAIGE.AI, Inc.

Inventor： Supriya KAPUR ,  Ran GODRICH ,  Christopher KANAN ,  Thomas FUCHS ,  Leo GRADY

IPC: G06T7/00 ,  G06T7/11 ,  G16H10/40 ,  G16H50/20 ,  G16H30/40 ,  G06V20/69 ,  G06F18/214

CPC classification number: G06T7/0012 ,  G06F18/214 ,  G06T7/11 ,  G06V20/695 ,  G06V20/698 ,  G16H10/40 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06V2201/03

Abstract: Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to identify a region of interest of the target specimen and determine an expression level of, category of, and/or presence of a biomarker in the region of interest, the biomarker comprising at least one from among an epithelial growth factor receptor (EGFR) biomarker and/or a DNA mismatch repair (MMR) deficiency biomarker, the machine learning system having been generated by processing a plurality of training images to predict whether a region of interest is present in the target electronic image, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the determined expression level of, category of, and/or presence of the biomarker in the region of interest.

公开(公告)号：US11715203B2

公开(公告)日：2023-08-01

申请号：US17221595

申请日：2021-04-02

Applicant: Tencent Technology (Shenzhen) Company Limited

Inventor： Liang Wang

IPC: G06T7/00 ,  G06T7/194 ,  G06T7/11 ,  G06F18/214 ,  G06V10/74 ,  G06V10/774 ,  G06V10/26 ,  A61B5/055 ,  A61B5/00

CPC classification number: G06T7/0014 ,  G06F18/214 ,  G06T7/11 ,  G06T7/194 ,  G06V10/26 ,  G06V10/761 ,  G06V10/774 ,  A61B5/055 ,  A61B5/4312 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/30068 ,  G06T2207/30096 ,  G06V2201/03

Abstract: An medical image processing method comprises generating a first segmented medical image in accordance with a first segmentation model and based on an original medical image that comprises a plurality of pixels. The method also comprises determining a foreground point and a background point according to the initial target region of the first segmented image. The method further comprises: for each pixel of the plurality of pixels of the original image, determining a first image distance between the respective pixel and the foreground point and a second image distance between the respective pixel and the background point. The method further comprises obtaining a foreground point range image and a background point range image corresponding to the original image, and generating a second segmented image in accordance with a second segmentation model based on the original image, the foreground point range image, and the background point range image.

公开(公告)号：US20230237782A1

公开(公告)日：2023-07-27

申请号：US18158658

申请日：2023-01-24

Applicant: MERATIVE US L.P.

Inventor： Murray A. Reicher ,  Aviad Zlotnick

IPC: G06V10/778 ,  G06F3/04842 ,  G06F18/24 ,  G06F18/40 ,  G06F18/21 ,  G06V30/194

CPC classification number: G06V10/7788 ,  G06F3/04842 ,  G06F18/24 ,  G06F18/41 ,  G06F18/217 ,  G06V30/194 ,  G06V2201/03

Abstract: Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes providing a visual concurrent display of a set of images of features, the features requiring classification by a reviewing user. The user interface is provided to enable the reviewing user to assign classifications to the images, the user interface being configured to create, read, update, and/or delete classifications. The user interface is responsive to the user, with the user response indicating at least two images with a single classification. The user interface is updated to represent the single classification.

公开(公告)号：US20230237646A1

公开(公告)日：2023-07-27

申请号：US17582469

申请日：2022-01-24

Applicant: Ruijin Hospital, Shanghai Jiao Tong University School of Medicine

Inventor： Guang Ning ,  Jiqiu Wang ,  Jie Hong ,  Juan Shi ,  Guoqing Bao

IPC: G06T7/00 ,  A61B6/03 ,  A61B6/00 ,  G06T7/11 ,  G06T11/00 ,  G06V10/44 ,  G06K9/62 ,  G06V10/82 ,  G06V10/50

CPC classification number: G06T7/0012 ,  A61B6/032 ,  A61B6/5205 ,  A61B6/5217 ,  G06T7/11 ,  G06T11/008 ,  G06V10/44 ,  G06K9/6256 ,  G06V10/82 ,  G06V10/50 ,  G06V2201/03 ,  G06T2207/10081 ,  G06T2207/30004 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A radiomic biomarker determination method and system for assessment of the risk of metabolic diseases. The method includes: obtaining abdominal & pelvic volumetric computed tomography (CT) scan from the given subject; determining the fat area to be analyzed from the CT scan, separating visceral fat using an image segmentation method, and normalizing the visceral fat area under physical scale; extracting N imaging features of the visceral fat; selecting n optimal imaging features from the N candidate features; dividing the normalized visceral fat area into multiple visceral fat blocks with equal thickness; extracting n corresponding optimal imaging features from each visceral fat block, named as block imaging features; and determining the representative visceral fat block from the candidate blocks and taking the representative visceral fat block and the (block) imaging features extracted from the representative visceral fat block as radiomic biomarkers.

公开(公告)号：US20230230355A1

公开(公告)日：2023-07-20

申请号：US18188837

申请日：2023-03-23

Applicant: TERUMO KABUSHIKI KAISHA

Inventor： Yasukazu SAKAMOTO ,  Katsuhiko SHIMIZU ,  Hiroyuki ISHIHARA ,  Shunsuke YOSHIZAWA ,  Thomas HENN ,  Clément JACQUET ,  Stephen TCHEN ,  Ryosuke SAGA

IPC: G06V10/764 ,  A61B8/12 ,  A61B8/08 ,  A61B8/00 ,  G06V10/774 ,  G06V10/22 ,  G06V10/82 ,  G06V10/12 ,  G06T17/00 ,  G06V20/70 ,  G06V10/776

CPC classification number: G06V10/764 ,  A61B8/12 ,  A61B8/483 ,  A61B8/466 ,  A61B8/469 ,  A61B8/463 ,  A61B8/0841 ,  A61B8/445 ,  A61B8/5223 ,  G06V10/774 ,  G06V10/22 ,  G06V10/82 ,  G06V10/12 ,  G06T17/00 ,  G06V20/70 ,  G06V10/776 ,  G06V2201/03

Abstract: An information processing device that includes: an image acquisition unit that acquires a catheter image obtained by an image acquisition catheter inserted into a first cavity; and a first classification data output unit configured to input the acquired catheter image to a first classification trained model that, upon receiving input of the catheter image, outputs first classification data in which a non-biological tissue region including a first inner cavity region that is inside the first cavity and a second inner cavity region that is inside a second cavity where the image acquisition catheter is not inserted and a biological tissue region are classified as different regions, and outputs the first classification data, in which the first classification trained model is generated using first training data that indicates at least the non-biological tissue region including the first inner cavity region and the second inner cavity region and the biological tissue region.