公开(公告)号：US11948221B2

公开(公告)日：2024-04-02

申请号：US17161660

申请日：2021-01-28

Applicant: Kenneth L. Weiss

Inventor： Kenneth L. Weiss

IPC: G06T7/11 ,  A61B5/00 ,  A61B5/026 ,  B60R25/00 ,  G06T7/00 ,  G16H20/10 ,  G16H30/40 ,  G16H70/60 ,  H04L12/40

CPC classification number: G06T7/0012 ,  A61B5/0036 ,  A61B5/0042 ,  A61B5/0263 ,  B60R25/00 ,  G06T7/11 ,  G16H20/10 ,  G16H30/40 ,  G16H70/60 ,  G06T2207/10016 ,  G06T2207/10072 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20101 ,  G06T2207/20221 ,  G06T2207/30004 ,  G06T2207/30012 ,  G06T2207/30016 ,  G06V2201/033 ,  H04L2012/40273 ,  Y10T428/24 ,  Y10T428/24744

Abstract: Image processing and analysis technique includes using a computer apparatus to assess a patient's computed tomography (CT) or magnetic resonance images for pathology and then automatically generate a prescription based at least in part on that assessment.

公开(公告)号：US11922684B2

公开(公告)日：2024-03-05

申请号：US17309395

申请日：2019-11-22

Applicant: metamorphosis GmbH

Inventor： Arno Blau

IPC: G06K9/00 ,  A61K35/12 ,  G06T7/13 ,  G06T7/73 ,  G06V10/44 ,  G06V10/94

CPC classification number: G06V10/95 ,  G06T7/13 ,  G06T7/75 ,  G06V10/44 ,  G06T2207/10116 ,  G06V2201/033

Abstract: The outline of a bone or at least a portion thereof may be determined based on deep neural net and optionally on an active shape model approach. An algorithm may detect a fracture of the bone. An algorithm may also classify the bone fracture and provide guidance on how to treat the fracture.

公开(公告)号：US20240046090A1

公开(公告)日：2024-02-08

申请号：US18484670

申请日：2023-10-11

Applicant: Medtronic Sofamor Danek USA, Inc.

Inventor： Saba Pasha

IPC: G06N3/08 ,  G06F18/22 ,  G06F18/23213 ,  G06N20/00 ,  G06T7/00 ,  G06T7/70 ,  G06T17/00 ,  G06V10/44 ,  G06V10/762 ,  G06V10/82 ,  G06V20/64 ,  G16H20/40 ,  G16H30/40 ,  G16H50/20 ,  A61B34/10

CPC classification number: G06N3/08 ,  G06F18/22 ,  G06F18/23213 ,  G06N20/00 ,  G06T7/0014 ,  G06T7/70 ,  G06T17/00 ,  G06V10/454 ,  G06V10/763 ,  G06V10/82 ,  G06V20/647 ,  G16H20/40 ,  G16H30/40 ,  G16H50/20 ,  A61B34/10 ,  G06V2201/033 ,  A61B34/30

Abstract: A spinal surgery training process includes the steps of capturing a plurality of 2D images for each of a plurality of spines, generating a curve of each spine from the respective 2D images based on locations of select vertebrae in each of the spines, grouping the spines into one of a number of groups based on similarity to produce groups of spines having similarities, performing the capturing, generating, determining and grouping steps at least once prior to surgery and at least once after surgery to produce pre-operative groups and their resultant post-operative groups, and assigning surgical methods and a probability to each of the post-operative groups indicating the probability that a spinal shape of the post-operative group can be achieved using the surgical methods. An outcome prediction process for determining surgical methods can be implemented once the training process is complete.

公开(公告)号：US11842275B2

公开(公告)日：2023-12-12

申请号：US17298718

申请日：2019-12-02

Applicant: AGFA NV

Inventor： Eva Vandersmissen

IPC: G06T7/00 ,  G06N3/08 ,  G06T7/12 ,  G06T7/143 ,  G06V10/94 ,  G06V10/25 ,  G06V10/75 ,  G06V10/764 ,  G06V10/776 ,  G06V10/80 ,  G06V10/82 ,  G06V10/48 ,  G06V10/26 ,  G06V10/46

CPC classification number: G06N3/08 ,  G06T7/12 ,  G06T7/143 ,  G06V10/25 ,  G06V10/26 ,  G06V10/46 ,  G06V10/48 ,  G06V10/757 ,  G06V10/764 ,  G06V10/776 ,  G06V10/809 ,  G06V10/82 ,  G06V10/95 ,  G06T2207/10116 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06V2201/033

Abstract: This invention is related to a method to improve the performance of a deep neural network (10) for the identification of a segmentation target (111) in a medical image (12, 110), comprising the steps of performing n training steps on said deep neural network (10) for the identification of said region of interest on two different representations (13, 14) of the same segmentation target (111), said representations (13,14) being definitions of the same segmentation target (111).

公开(公告)号：US11841923B2

公开(公告)日：2023-12-12

申请号：US17366480

申请日：2021-07-02

Applicant: Alibaba Group Holding Limited

Inventor： Tao Jiang ,  Yu Wang ,  Ying Chi ,  Lei Zhang ,  Xiansheng Hua

IPC: G06K9/00 ,  G06F18/21 ,  G06T7/73 ,  G16H30/40 ,  G06N3/08 ,  G06T7/00 ,  G06F18/214

CPC classification number: G06F18/2178 ,  G06F18/214 ,  G06N3/08 ,  G06T7/0014 ,  G06T7/73 ,  G16H30/40 ,  G06T2200/24 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30012 ,  G06V2201/033

Abstract: The present application discloses a method, device, and system for processing a medical image. The method includes obtaining a source spinal image, identifying one or more vertebral bodies and one or more intervertebral discs comprised in the source spinal image, determining the vertebral body recognition results corresponding to the one or more vertebral bodies and the intervertebral disc recognition results corresponding to the one or more intervertebral discs, and determining target recognition results corresponding to the source spinal image based at least in part one on one or more of the vertebral body recognition results and the intervertebral disc recognition results.

公开(公告)号：US11790643B2

公开(公告)日：2023-10-17

申请号：US17246504

申请日：2021-04-30

Applicant: Align Technology, Inc.

Inventor： Ya Xue ,  Yingjie Li ,  Chao Shi ,  Aleksandr Anikin ,  Mikhail Toporkov ,  Aleksandr Sergeevich Karsakov

IPC: G06F18/2413 ,  G06V30/19 ,  G06T7/13 ,  G06T7/00 ,  G06V10/82 ,  G06V10/44

CPC classification number: G06V10/82 ,  G06F18/24143 ,  G06T7/0012 ,  G06T7/13 ,  G06V10/44 ,  G06V30/19173 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20132 ,  G06T2207/30004 ,  G06V2201/033

Abstract: A method includes receiving an image of a face of a patient, the image including a depiction of teeth; processing the image of the face using one or more trained machine learning model, wherein the one or more trained machine learning model outputs a pixel-level classification of pixels in the image, the pixel level classification comprising a first set of pixels classified as being inside of a bounding shape that bounds a first plurality of teeth depicted in the image and a second set of pixels classified as being outside of the first bounding shape; cropping the image of the face of the patient, wherein the cropped image comprises depictions of the first plurality of teeth; and performing one or more operations on the cropped image of the face of the patient.

公开(公告)号：US11786129B2

公开(公告)日：2023-10-17

申请号：US17666319

申请日：2022-02-07

Applicant: SHANGHAI UNITED IMAGING INTELLIGENCE CO., LTD.

Inventor： Srikrishna Karanam ,  Ziyan Wu ,  Georgios Georgakis

IPC: G06K9/00 ,  A61B5/00 ,  G16H30/40 ,  G06T7/00 ,  G06T7/90 ,  G06T17/00 ,  G06T7/50 ,  G06T7/70 ,  G06T17/20 ,  G16H10/60 ,  G16H30/20 ,  G06V20/64 ,  G06V40/10 ,  G06V40/20 ,  G06V20/62 ,  G06F18/21 ,  G06F18/214 ,  G06V10/764 ,  G06V10/774 ,  G06V10/778 ,  G06V10/82 ,  G06V10/42 ,  G06V10/40

CPC classification number: A61B5/0077 ,  A61B5/0035 ,  A61B5/70 ,  G06F18/21 ,  G06F18/214 ,  G06F18/2193 ,  G06T7/0012 ,  G06T7/50 ,  G06T7/70 ,  G06T7/90 ,  G06T17/00 ,  G06T17/20 ,  G06V10/40 ,  G06V10/42 ,  G06V10/764 ,  G06V10/774 ,  G06V10/7796 ,  G06V10/82 ,  G06V20/62 ,  G06V20/64 ,  G06V40/10 ,  G06V40/20 ,  G16H10/60 ,  G16H30/20 ,  G16H30/40 ,  G06T2200/08 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30196 ,  G06V2201/033

Abstract: Human mesh model recovery may utilize prior knowledge of the hierarchical structural correlation between different parts of a human body. Such structural correlation may be between a root kinematic chain of the human body and a head or limb kinematic chain of the human body. Shape and/or pose parameters relating to the human mesh model may be determined by first determining the parameters associated with the root kinematic chain and then using those parameters to predict the parameters associated with the head or limb kinematic chain. Such a task can be accomplished using a system comprising one or more processors and one or more storage devices storing instructions that, when executed by the one or more processors, cause the one or more processors to implement one or more neural networks trained to perform functions related to the task.

公开(公告)号：US11749406B2

公开(公告)日：2023-09-05

申请号：US16947844

申请日：2020-08-20

Applicant: Siemens Healthcare GmbH

Inventor： Kevin Brown ,  Matthias Wolf

IPC: G06K9/00 ,  G16H50/20 ,  G06T15/08 ,  G06T7/30 ,  G06T7/11 ,  G06T11/00 ,  G06N3/08 ,  G16H30/40 ,  G06F18/21 ,  G06V10/82 ,  G06V10/44

CPC classification number: G16H50/20 ,  G06F18/2163 ,  G06N3/08 ,  G06T7/11 ,  G06T7/30 ,  G06T11/003 ,  G06T15/08 ,  G06V10/454 ,  G06V10/82 ,  G16H30/40 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30012 ,  G06V2201/033

Abstract: A system and method include identification of a plurality of sub-volumes of an image volume, each of the plurality of sub-volumes of the image volume associated with a respective one of a plurality of vertebra, registration of each of the plurality of sub-volumes of the image volume to one of a plurality of reference sub-volumes associated with a same respective vertebra, input of the registered sub-volumes and the image volume to a trained neural network, reception of an output image volume from the trained neural network, the output image volume labeled to associate voxels with respective vertebra, and display of the output image volume.

公开(公告)号：US20230233106A1

公开(公告)日：2023-07-27

申请号：US18011011

申请日：2021-06-17

Applicant: Kneedly AB

Inventor： Martin Fagerström ,  Alice Nilsson ,  Joel Nilsson ,  Alexander Rydevald ,  Kristian Samuelsson ,  Eric Hamrin Senorski ,  Linn Söderholm

IPC: A61B5/11 ,  G06V10/75 ,  G06V10/22 ,  G06T7/00 ,  G06V10/25 ,  G06T7/246

CPC classification number: A61B5/1128 ,  G06V10/755 ,  G06V10/23 ,  G06T7/0012 ,  G06V10/25 ,  G06T7/248 ,  G06V2201/033

Abstract: A solution for non-invasive determination of supraphysiological body joint kinematics. The solution obtains external images related to a test procedure of the body joint and performs image analysis on the obtained images to define a pattern of a plurality of spatial points in a region of interest. Each individual spatial point is defined by a unique pattern of neighboring surrounding pixels in each image, and the pattern is part of a high-contrast speckle pattern applied to the body joint. The solution identifies displacements of the spatial points in subsequently obtained images by tracing a location of the unique pattern of neighboring pixels in each image in relation to a base image of the body joint, calculates deformation measures from the displacements of the plurality of spatial points, and obtains deformation measures of a reference body joint. The solution compares the deformation measures and determines supraphysiological body joint kinematics from the comparison.

公开(公告)号：US20240233330A1

公开(公告)日：2024-07-11

申请号：US18406883

申请日：2024-01-08

Applicant: NATIONAL TAIWAN UNIVERSITY

Inventor： Wen-Shiang CHEN ,  Chung-Ping CHEN ,  Hsin-Yuan CHU

IPC: G06V10/764 ,  A61B8/00 ,  A61B8/08 ,  G06V10/77 ,  G06V10/774 ,  G06V10/776

CPC classification number: G06V10/764 ,  A61B8/461 ,  A61B8/469 ,  A61B8/5223 ,  G06V10/7715 ,  G06V10/7753 ,  G06V10/776 ,  G06V2201/033

Abstract: Provided are an ultrasound image detection system and a method thereof based on artificial intelligence (AI) automatic labeling of anatomical structures, including: a receiving module, an image recognition module having an object detection model, an image processing module and a display module, wherein the image recognition module utilizes the object detection model to perform object detection on the image to be recognized, which is received by the receiving module, and then obtains a plurality of object recognition images with object detection results. Then, the image processing module detects missed anatomical structures according to the object detection results of the plurality of object recognition images, thereby outputting an object detection image. Additionally, the display module displays the object detection image. Therefore, the anatomical structures in the ultrasound image can be automatically and instantly recognized by AI so as to provide accurate judgment basis for medical personnel.