公开(公告)号：US20240013411A1

公开(公告)日：2024-01-11

申请号：US18347569

申请日：2023-07-06

Applicant: MEDICALIP CO., LTD.

Inventor： Jong Min KIM ,  Khwaja Monib SEDIQI ,  Sang Joon PARK

IPC: G06T7/30 ,  G06V10/764 ,  G06T7/00

CPC classification number: G06T7/30 ,  G06V10/764 ,  G06T7/0014 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20084 ,  G06V2201/03

Abstract: A medical image registration method and apparatus are provided. Upon receiving a medical image captured using a contrast agent, the medical image registration apparatus determines a phase of the medical image by using a classification model, and registers the medical image to a reference image by using a registration model.
The disclosure refers to a technology developed through the “Korea Data and Software-Driven Hospitals Consortium 2.0)” project supervised by Seoul National University Bundang Hospital (task number: S0252-21-1001).

公开(公告)号：US11869670B2

公开(公告)日：2024-01-09

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

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

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：US11861501B2

公开(公告)日：2024-01-02

申请号：US17074629

申请日：2020-10-20

Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

Inventor： Sihong Chen

IPC: G06N3/08 ,  G06V10/26 ,  G06V10/30 ,  G06V10/764 ,  G06V10/82 ,  G06V20/64 ,  G06N3/084 ,  G06V20/40 ,  G06N3/045 ,  G06V10/44

CPC classification number: G06N3/084 ,  G06N3/045 ,  G06N3/08 ,  G06V10/26 ,  G06V10/30 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V20/41 ,  G06V20/64 ,  G06V2201/03

Abstract: A semantic segmentation method and apparatus for a three-dimensional image, and a storage medium are provided. The method includes: obtaining a three-dimensional image; slicing the three-dimensional image according to three directional planes, to obtain two-dimensional slice images of an x axis, two-dimensional slice images of a y axis, and two-dimensional slice images of a z axis; invoking a first segmentation model, a second segmentation model, and a third segmentation model to respectively perform semantic segmentation on the two-dimensional slice images of the x axis, the y axis, and the z axis, to obtain distribution probability maps of a target object on the three directional planes; and obtaining a three-dimensional distribution binary image of the target object by invoking an adaptive fusion model to perform three-dimensional fusion on the three distribution probability maps respectively corresponding to an x-axis directional plane, a y-axis directional plane, and a z-axis directional plane.

公开(公告)号：US11854201B2

公开(公告)日：2023-12-26

申请号：US17101267

申请日：2020-11-23

Applicant: NIKON CORPORATION

Inventor： Kohki Konishi ,  Mitsuo Suga ,  Hideo Nishioka

IPC: G06K9/00 ,  G06T7/00 ,  G01N33/483 ,  H01J37/20 ,  H01J37/22 ,  H01J37/28 ,  G06V20/69 ,  G06V10/764 ,  G06V10/82 ,  G06V10/44 ,  G01N23/2251

CPC classification number: G06T7/0014 ,  G01N33/4833 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V20/693 ,  G06V20/695 ,  G06V20/698 ,  H01J37/20 ,  H01J37/222 ,  H01J37/28 ,  G01N23/2251 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06V2201/03 ,  H01J2237/202 ,  H01J2237/221

Abstract: A current observation area is determined exploratorily from among a plurality of candidate areas, on the basis of a plurality of observed areas in a biological tissue. A plurality of reference images obtained by means of low-magnification observation of the biological tissue are utilized at this time. A learning image is acquired by means of high-magnification observation of the determined current observation area. A plurality of convolution filters included in an estimator can be utilized to evaluate the plurality of candidate areas.

公开(公告)号：US20230410993A1

公开(公告)日：2023-12-21

申请号：US18338102

申请日：2023-06-20

Applicant: MAKO Surgical Corporation

Inventor： Arman MOTESHAREI ,  Nathalie WILLEMS ,  Alison LONG ,  Daniele DE MASSARI ,  Hyosig KANG

IPC: G16H40/20 ,  G16H50/30 ,  G16H50/70 ,  G16H20/40 ,  G06T7/00 ,  G06T7/62 ,  G06V20/60 ,  A61B6/03 ,  A61B6/00

CPC classification number: G16H40/20 ,  G16H50/30 ,  G16H50/70 ,  G16H20/40 ,  G06T7/0012 ,  G06T7/62 ,  G06V20/60 ,  A61B6/032 ,  A61B6/54 ,  G06T2207/30008 ,  G06T2207/10081 ,  G06V2201/03

Abstract: Aspects disclosed herein may provide a method for determining a duration of a medical procedure. The method may include receiving imaging data including at least one image acquired of a patient's anatomy, determining at least one parameter of the patient's anatomy based on the imaging data, predicting a duration for the medical procedure based on the determined at least one parameter, and outputting the predicted duration on an electronic display. The at least one parameter may include at least one of a B-score, a joint-space width, an osteophyte position or volume, an alignment, or a deformity based on the imaging data.

公开(公告)号：US20230410986A1

公开(公告)日：2023-12-21

申请号：US18458532

申请日：2023-08-30

Applicant: PAIGE.AI, Inc.

Inventor： Ran GODRICH ,  Jillian SUE ,  Leo GRADY ,  Thomas FUCHS

IPC: G16H30/40 ,  G16H70/60 ,  G16H40/20 ,  G16H10/40 ,  G16H50/20 ,  G16H70/20 ,  G16B40/20 ,  G06N20/00 ,  G06T7/00 ,  G06F18/214

CPC classification number: G16H30/40 ,  G16H70/60 ,  G16H40/20 ,  G16H10/40 ,  G16H50/20 ,  G16H70/20 ,  G16B40/20 ,  G06N20/00 ,  G06T7/0012 ,  G06F18/214 ,  G06V2201/03 ,  G06V2201/04 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06T2207/30204

Abstract: Systems and methods are disclosed for processing images including, for example, receiving a target image of a slide corresponding to a target specimen comprising a tissue sample of a patient; determining a quality control metric for the target image via a first trained machine learning model having been trained to predict the quality control metric based on the target image, wherein the quality control metric signifies a quality control issue; and outputting, via a user interface, a sequence of a plurality of digitized pathology images, wherein a placement of the target image in the sequence is based on the quality control metric.

公开(公告)号：US20230410499A1

公开(公告)日：2023-12-21

申请号：US18035989

申请日：2021-11-12

Applicant: Intuitive Surgical Operations, Inc.

Inventor： Omid Mohareri ,  Adam T. Schmidt ,  Aidean Sharghi Karganroodi

IPC: G06V10/98 ,  G06V10/12 ,  G06V10/764 ,  G06V10/774 ,  H04N23/695 ,  H04N23/60 ,  H04N23/90 ,  G06V40/20

CPC classification number: G06V10/993 ,  G06V10/12 ,  G06V10/764 ,  G06V10/774 ,  H04N23/695 ,  H04N23/64 ,  H04N23/90 ,  G06V40/20 ,  G06V2201/03

Abstract: Visibility metrics in multi-view medical activity recognition systems and methods are described herein. In certain illustrative examples, a system access imagery of a scene of a medical session captured by a plurality of sensors from a plurality of viewpoints, the imagery including first imagery captured by a first sensor of the plurality of sensors from a first viewpoint of the plurality of viewpoints. The system determines, during the medical session and based on the first imagery, a value of an activity visibility metric for the first sensor. The system facilitates, based on the value of the activity visibility metric, adjusting the first viewpoint of the first sensor.

公开(公告)号：US20230410480A1

公开(公告)日：2023-12-21

申请号：US17897226

申请日：2022-08-29

Applicant: Wistron Corporation

Inventor： Guan Yi Chian ,  Kuan-I Chung

IPC: G06V10/774 ,  G06V10/96 ,  G06V10/22 ,  G06T7/00 ,  G06V10/26 ,  G06V10/32 ,  G06V10/776

CPC classification number: G06V10/774 ,  G06V10/96 ,  G06V10/225 ,  G06T7/0012 ,  G06V10/267 ,  G06V10/32 ,  G06V10/776 ,  G06V2201/03 ,  G06T2207/30096 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: An embodiment of the invention provides a processing method of a medical image and a computing apparatus for processing a medical image. In the method, one or more image samples are obtained, a tumor region and an appearance feature thereof in the image sample are marked, and an image recognition model is trained according to the image sample, the tumor region thereof, and an appearance feature of a first tumor. The image sample is an image obtained by photographing an animal body. The appearance feature represents an appearance of the first tumor corresponding to the tumor region. The image recognition model identifies a second tumor in an image to be evaluated is a first type. The first type is related to the tumor region and the appearance feature of the first tumor. Accordingly, a prediction accuracy may be improved.

公开(公告)号：US11844499B2

公开(公告)日：2023-12-19

申请号：US18165232

申请日：2023-02-06

Applicant: COSMO ARTIFICIAL INTELLIGENCE—AI LIMITED

Inventor： Nhan Ngo Dinh ,  Giulio Evangelisti ,  Flavio Navari

IPC: G06K9/62 ,  A61B1/31 ,  G06T7/70 ,  G16H30/20 ,  G06N3/08 ,  G06T7/00 ,  G06T11/00 ,  G06T11/60 ,  G06T11/20 ,  A61B1/00 ,  A61B5/00 ,  G06V20/40 ,  G06V10/82 ,  G06F18/214 ,  G06N3/045 ,  G06V10/25 ,  G06V10/20

CPC classification number: A61B1/31 ,  A61B1/00055 ,  A61B1/000094 ,  A61B1/000095 ,  A61B1/000096 ,  A61B5/7264 ,  A61B5/7267 ,  G06F18/214 ,  G06N3/045 ,  G06N3/08 ,  G06T7/0012 ,  G06T7/70 ,  G06T11/001 ,  G06T11/203 ,  G06T11/60 ,  G06V10/25 ,  G06V10/255 ,  G06V10/82 ,  G06V20/40 ,  G06V20/49 ,  G16H30/20 ,  G06T2207/10016 ,  G06T2207/10068 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30032 ,  G06T2207/30064 ,  G06T2207/30096 ,  G06V2201/03 ,  G06V2201/032

Abstract: The present disclosure relates to systems and methods for processing real-time video and detecting objects in the video. In one implementation, a system is provided that includes an input port for receiving real-time video obtained from a medical image device, a first bus for transferring the received real-time video, and at least one processor configured to receive the real-time video from the first bus, perform object detection by applying a trained neural network on frames of the received real-time video, and overlay a border indicating a location of at least one detected object in the frames. The system also includes a second bus for receiving the video with the overlaid border, an output port for outputting the video with the overlaid border from the second bus to an external display, and a third bus for directly transmitting the received real-time video to the output port.

公开(公告)号：US11842490B2

公开(公告)日：2023-12-12

申请号：US18191088

申请日：2023-03-28

Applicant: Zhejiang University

Inventor： Kai Jin ,  Juan Ye ,  Zhiyuan Gao ,  Xiaoyu Ma ,  Yaqi Wang ,  Yunxiang Li

IPC: G06T7/00 ,  G06V10/80 ,  G06V20/70 ,  G06V10/44 ,  G06V10/764 ,  G06T3/40 ,  G06V10/774 ,  G06T7/11

CPC classification number: G06T7/0012 ,  G06T3/40 ,  G06T7/11 ,  G06V10/44 ,  G06V10/764 ,  G06V10/774 ,  G06V10/806 ,  G06V20/70 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20132 ,  G06T2207/30041 ,  G06T2207/30168 ,  G06V2201/03

Abstract: Disclosed is a fundus image quality evaluation method based on multi-source and multi-scale feature fusion, comprising following steps: S1, acquiring multi-source fundus images, labeling the multi-source fundus images with four evaluation dimensions of brightness, blur, contrast and overall image quality, and forming training samples with the fundus image and labeling labels; S2, constructing a fundus image quality evaluation network including a feature extraction module, a fusion module, an attention module and an evaluation module; S3, training the fundus image quality evaluation network by using training samples to obtain a fundus image quality evaluation model; and S4: inputting fundus images to be measured into the fundus image quality evaluation model, and outputting quality evaluation results through calculation. Also provided is a fundus image quality evaluation device based on above method.