公开(公告)号：US12067724B2

公开(公告)日：2024-08-20

申请号：US17186814

申请日：2021-02-26

Applicant: StraxCorp Pty. Ltd.

Inventor： Yu Peng

IPC: G06T7/00 ,  G06F18/21 ,  G06F18/214 ,  G06T7/11

CPC classification number: G06T7/11 ,  G06F18/2148 ,  G06F18/2155 ,  G06F18/217 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06V2201/03

Abstract: An image segmentation method system, the system comprising: a training subsystem configured to train a segmentation machine learning model using annotated training data comprising images associated with respective segmentation annotations, so as to generate a trained segmentation machine learning model; a model evaluator; and a segmentation subsystem configured to perform segmentation of a structure or material in an image using the trained segmentation machine learning model. The model evaluator is configured to evaluate the segmentation machine learning model by (i) controlling the segmentation subsystem to segment at least one evaluation image associated with an existing segmentation annotation using the segmentation machine learning model and thereby generate a segmentation of the annotated evaluation image, and (ii) forming a comparison of the segmentation of the annotated evaluation image and the existing segmentation annotation. The method includes deploying the trained segmentation machine learning model for use if the comparison indicates that the segmentation machine learning model is satisfactory.

公开(公告)号：US20240273925A1

公开(公告)日：2024-08-15

申请号：US18645796

申请日：2024-04-25

Applicant: J. MORITA MFG. CORP.

Inventor： Manabu Hashimoto ,  Ryosuke Kaji ,  Mikinori Nishimura

IPC: G06V20/64 ,  A61B1/24 ,  A61B5/00 ,  G06F18/214 ,  G06V10/40 ,  G16H30/40 ,  G16H50/50

CPC classification number: G06V20/64 ,  A61B1/24 ,  A61B5/0062 ,  A61B5/0088 ,  G06F18/214 ,  G06V10/40 ,  G16H30/40 ,  G16H50/50 ,  G06V2201/03

Abstract: An identification device that identifies a type of a tooth includes: an input unit that receives three-dimensional data including three-dimensional position information at each of a plurality of points forming the tooth; an identification unit that identifies a type of the tooth based on the three-dimensional data received by the input unit and an estimation model including a neural network; and an output unit that outputs an identification result obtained by the identification unit. The identification unit directly inputs the three-dimensional position information included in the three-dimensional data received by the input unit to the neural network included in the estimation model.

公开(公告)号：US20240266054A1

公开(公告)日：2024-08-08

申请号：US18595563

申请日：2024-03-05

Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

Inventor： Kuan TIAN ,  Cheng JIANG

IPC: G16H50/20 ,  G06F18/22 ,  G06N20/00 ,  G06T7/00 ,  G06T7/11 ,  G06T7/62 ,  G06V10/10 ,  G06V10/25 ,  G06V10/26 ,  G06V10/82 ,  G06V30/19 ,  G06V30/262 ,  G16H30/20 ,  G16H30/40 ,  G16H50/70

CPC classification number: G16H50/20 ,  G06F18/22 ,  G06N20/00 ,  G06T7/0014 ,  G06T7/11 ,  G06T7/62 ,  G06V10/17 ,  G06V10/25 ,  G06V10/26 ,  G06V10/82 ,  G06V30/19147 ,  G06V30/19153 ,  G06V30/19173 ,  G06V30/274 ,  G16H30/20 ,  G16H30/40 ,  G16H50/70 ,  G06T2207/20021 ,  G06T2207/30096 ,  G06V2201/03

Abstract: A medical image processing method includes: obtaining a biological tissue image including a biological tissue, recognizing, in the biological tissue image, a first region of a lesion object in the biological tissue; recognizing a lesion attribute matching the lesion object; dividing an image region of the biological tissue in the biological tissue image into a plurality of quadrant regions; obtaining target quadrant position information of a quadrant region in which the first region is located; and generating medical service data according to the target quadrant position information and the lesion attribute.

公开(公告)号：US20240265542A1

公开(公告)日：2024-08-08

申请号：US18163359

申请日：2023-02-02

Applicant: The Chinese University of Hong Kong

Inventor： Carol Yim Lui CHEUNG ,  Clement Chee Yung THAM ,  Anran RAN ,  Pheng Ann HENG ,  Xi WANG

IPC: G06T7/00 ,  A61B3/00 ,  A61B3/10 ,  A61B3/12 ,  G06V10/764 ,  G16H30/40 ,  G16H50/20

CPC classification number: G06T7/0014 ,  A61B3/0025 ,  A61B3/102 ,  A61B3/12 ,  G06V10/764 ,  G16H30/40 ,  G16H50/20 ,  G06T2200/04 ,  G06T2200/24 ,  G06T2207/10101 ,  G06T2207/20081 ,  G06T2207/30041 ,  G06T2207/30168 ,  G06V2201/03

Abstract: The subject invention pertains to an artificial intelligence-aided classification system for glaucomatous optic neuropathy (GON) and myopic optic disc morphology (myopic features, MF) from three-dimensional (3D) optical coherence tomography (OCT) scans, which includes a deep-learning (DL) based “pre-diagnosis model” for image quality control and a multi-task DL-based classification and visualization model for GON and MF detection, including heatmaps for visualizing the identified features. The invention provides an Al-platform with the integration of developed 3D DL algorithms, an information management system, connecting to a commercially available OCT device. This Al-platform includes a user interface for real-time OCT image extraction, input data configuration, image uploading, images analysis via a graphics processing unit (GPU) server, and Al reports generation. The platform provides outputs including image quality, GON classification, MF classification, AI scores, and referral suggestion.

公开(公告)号：US20240265531A1

公开(公告)日：2024-08-08

申请号：US18494817

申请日：2023-10-26

Applicant: Canon Medical Systems Corporation

Inventor： Asateru KIMURA ,  Sho SASAKI ,  Minoru NAKATSUGAWA

IPC: G06T7/00 ,  G06V10/776

CPC classification number: G06T7/0012 ,  G06V10/776 ,  G06T2207/30096 ,  G06V2201/03

Abstract: According to one embodiment, a medical information processing apparatus includes processing circuitry. The processing circuitry acquires first and second medical images acquired in at least first and second time phases. The processing circuitry inputs the first and second medical images into a gene mutation classification model to generate first and second gene mutation classification results. The processing circuitry judges consistency of the gene mutation classification model based on the first and second gene mutation classification results.

公开(公告)号：US20240259534A1

公开(公告)日：2024-08-01

申请号：US18629965

申请日：2024-04-09

Applicant: FUJIFILM Corporation

Inventor： Masaaki OOSAKE

IPC: H04N7/18 ,  A61B1/00 ,  G06T7/00 ,  G06T7/60 ,  G06T7/70 ,  G06T11/00 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  H04N23/50

CPC classification number: H04N7/183 ,  A61B1/00006 ,  A61B1/000094 ,  A61B1/0005 ,  G06T7/0012 ,  G06T7/60 ,  G06T7/70 ,  G06T11/001 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  H04N23/50 ,  G06T2207/10068 ,  G06T2207/30004 ,  G06V2201/03 ,  H04N23/555

Abstract: Provided are a medical image processing device, a medical image processing method, and an endoscope system that make it easy to compare a region of interest and its peripheral region with each other and make it unlikely to miss the region of interest if the region of interest in a time-series image is reported by using figures. A coordinates calculating unit (43) that calculates, on the basis of region-of-interest information indicating a region of interest in a time-series image, a plurality of sets of coordinates of interest on an outline of a polygon or circle having a symmetric shape that surrounds the region of interest. A reporting information display control unit (45B) that superposes figures on the basis of the calculated plurality of sets of coordinates of interest when superposing the figures for reporting the region of interest on the time-series image. Herein, the figures have a size that does not change with respect to a size of the region of interest.

公开(公告)号：US20240257977A1

公开(公告)日：2024-08-01

申请号：US18627817

申请日：2024-04-05

Applicant: PAIGE.AI, Inc. ,  Memorial Sloan-Kettering Cancer Center

Inventor： Leo GRADY ,  Christopher KANAN ,  Jorge Sergio REIS-FILHO ,  Belma DOGDAS ,  Matthew HOULISTON

IPC: G16H50/20 ,  G06F18/214 ,  G06T7/00 ,  G06V10/25 ,  G06V30/19 ,  G16H10/20 ,  G16H30/40

CPC classification number: G16H50/20 ,  G06F18/214 ,  G06T7/0012 ,  G06V10/25 ,  G06V30/19147 ,  G16H10/20 ,  G16H30/40 ,  G06T2207/20081 ,  G06T2207/30004 ,  G06V2201/03

Abstract: Systems and methods are disclosed for processing digital images to identify diagnostic tests, the method comprising receiving one or more digital images associated with a pathology specimen, determining a plurality of diagnostic tests, applying a machine learning system to the one or more digital images to identify any prerequisite conditions for each of the plurality of diagnostic tests to be applicable, the machine learning system having been trained by processing a plurality of training images, identifying, using the machine learning system, applicable diagnostic tests of the plurality of diagnostic tests based on the one or more digital images and the prerequisite conditions, and outputting the applicable diagnostic tests to a digital storage device and/or display.

公开(公告)号：US20240249406A1

公开(公告)日：2024-07-25

申请号：US18289856

申请日：2022-05-13

Applicant: KCI Manufacturing Unlimited Company

Inventor： Chester EDLUND ,  Brian LAWRENCE ,  Christopher J. SANDROUSSI ,  James LAIRD

IPC: G06T7/00 ,  A61B5/00 ,  G06T3/40 ,  G06T7/11 ,  G06T7/13 ,  G06T7/136 ,  G06T7/50 ,  G06T7/62 ,  G06V10/771 ,  G06V10/82 ,  H04N23/60 ,  H04N23/63

CPC classification number: G06T7/0012 ,  A61B5/0077 ,  A61B5/7455 ,  A61B5/7475 ,  G06T3/40 ,  G06T7/11 ,  G06T7/13 ,  G06T7/136 ,  G06T7/50 ,  G06T7/62 ,  G06V10/771 ,  G06V10/82 ,  H04N23/631 ,  H04N23/635 ,  H04N23/64 ,  G06T2207/10028 ,  G06T2207/10048 ,  G06T2207/20084 ,  G06T2207/30088 ,  G06V2201/03

Abstract: A system for measuring a wound site including an image capture device, a touchscreen, a vibration motor, and a processor. The image capture device may be configured to capture a digital image and capture a depth map associated with the digital image. The processor may be configured to determine abounding box of the wound site, determine a wound mask, determine a wound boundary, determine whether the wound boundary is aligned within a camera frame, and generate a wound map. The processor and vibration motor may be configured to provide a series of vibrations in response to the processor determining that the wound is aligned within the camera frame.

公开(公告)号：US20240248964A1

公开(公告)日：2024-07-25

申请号：US18444143

申请日：2024-02-16

Applicant: Siemens Healthineers AG

Inventor： Alexander MUEHLBERG ,  Oliver TAUBMANN ,  Alexander KATZMANN ,  Felix DURLAK ,  Michael WELS ,  Felix LADES ,  Rainer KAERGEL ,  Michael SUEHLING

IPC: G06F18/2433 ,  G06F18/21 ,  G06F18/245 ,  G06T7/00

CPC classification number: G06F18/2433 ,  G06F18/2193 ,  G06F18/245 ,  G06T7/0012 ,  G06T2207/20076 ,  G06V2201/03

Abstract: A computer-implemented method is for providing radiomics-related information. In an embodiment, the computer-implemented method includes receiving radiomics-related data; determining, based on the radiomics-related data and an assistance algorithm, a function for processing the radiomics-related data; calculating, based on the radiomics-related data and the function for processing the radiomics-related data, the radiomics-related information; and providing the radiomics-related information.

公开(公告)号：US20240242818A1

公开(公告)日：2024-07-18

申请号：US18418048

申请日：2024-01-19

Applicant: Verb Surgical Inc.

Inventor： Jagadish Venkataraman ,  Pablo E. Garcia Kilroy

IPC: G16H30/40 ,  G06N20/00 ,  G06V20/40 ,  G06V20/70 ,  G16H30/20

CPC classification number: G16H30/40 ,  G06N20/00 ,  G06V20/41 ,  G06V20/44 ,  G06V20/46 ,  G06V20/49 ,  G06V20/70 ,  G16H30/20 ,  G06V2201/03

Abstract: Embodiments described herein provide various examples of a surgical video analysis system for segmenting surgical videos of a given surgical procedure into shorter video segments and labeling/tagging these video segments with multiple categories of machine learning descriptors. In one aspect, a process for processing surgical videos recorded during performed surgeries of a surgical procedure includes the steps of: receiving a diverse set of surgical videos associated with the surgical procedure; receiving a set of predefined phases for the surgical procedure and a set of machine learning descriptors identified for each predefined phase in the set of predefined phases; for each received surgical video, segmenting the surgical video into a set of video segments based on the set of predefined phases and for each segment of the surgical video of a given predefined phase, annotating the video segment with a corresponding set of machine learning descriptors for the given predefined phase.