公开(公告)号：US20240206726A1

公开(公告)日：2024-06-27

申请号：US18544635

申请日：2023-12-19

Applicant: Ziemer Ophthalmic Systems AG

Inventor： Thomas Asshauer ,  Christian Rathjen ,  Franziska Rothen ,  Michael Steinlechner

IPC: A61B3/10 ,  A61F9/008 ,  G06T7/00 ,  G06T15/08 ,  G06T17/00 ,  G06V10/22 ,  G06V10/764

CPC classification number: A61B3/102 ,  A61F9/008 ,  G06T7/0012 ,  G06T15/08 ,  G06T17/00 ,  G06V10/22 ,  G06V10/764 ,  A61F2009/00872 ,  G06T2207/10101 ,  G06T2207/20084 ,  G06T2207/30041 ,  G06T2210/41 ,  G06V2201/03

Abstract: A computer-implemented method and device for characterizing an optical inhomogeneity in a human eye is disclosed, the method comprising: receiving optical coherence tomography data of the eye; receiving image data of an image of the eye recorded by a camera, the image recorded using one or more of the following imaging techniques: direct illumination of the eye, retro-illumination of the eye, or Scheimpflug imaging; and characterizing the optical inhomogeneity as one or more of the following optical inhomogeneity types: a cataract, a floater, or an opacification of the cornea using the optical coherence tomography data and the image data.

公开(公告)号：US20240203552A1

公开(公告)日：2024-06-20

申请号：US18539090

申请日：2023-12-13

Applicant: Stryker Corporation

Inventor： Jose GEORGE ,  Sanskruti MASKE

IPC: G16H15/00 ,  G06V10/74 ,  G06V20/40 ,  G10L13/033

CPC classification number: G16H15/00 ,  G06V10/761 ,  G06V20/41 ,  G06V20/46 ,  G10L13/033 ,  G06V2201/03

Abstract: Disclosed herein are methods for generating a video surgical report using a machine learning pipeline. The machine learning pipeline may include one or more machine learning models, each of which may support a particular aspect of a video surgical report generation process. For example, one or more images of a surgical procedure may be obtained. Using one or more machine learning models, a set of images from the one or more images may be selected based on the surgical procedure. A video surgical report may be generated for the surgical procedure, which may include at least some of the set of images. The machine learning pipeline can offload work typically performed by a user (e.g., surgeon, medical staff, etc.) to create the video surgical report, thereby saving significant time and/or resources.

公开(公告)号：US20240203108A1

公开(公告)日：2024-06-20

申请号：US17802953

申请日：2022-02-28

Applicant: ZHEJIANG UNIVERSITY

Inventor： JING WANG ,  BO DONG ,  HONGJIAN HE ,  XIUJUN CAI

IPC: G06V10/80 ,  A61B34/10 ,  G06V10/26 ,  G06V10/74 ,  G06V10/77 ,  G06V10/776 ,  G06V20/70

CPC classification number: G06V10/806 ,  A61B34/10 ,  G06V10/26 ,  G06V10/761 ,  G06V10/7715 ,  G06V10/776 ,  G06V20/70 ,  A61B2034/107 ,  G06V2201/03

Abstract: The present invention discloses a decoupling divide-and-conquer facial nerve segmentation method and device. As for the characteristics of a small facial nerve structure and a low contrast, a facial nerve segmentation model including a feature extraction module, a rough segmentation module, and a fine segmentation module is constructed. The feature extraction module is configured to extract a low-level feature and a plurality of different-and high-level features. The rough segmentation module is configured to globally search the different-and high-level features for facial-nerve features and fuse them. The fine segmentation module is configured to decouple a fused feature to obtain a central body feature. After the central body feature is combined with the low-level feature to obtain an edge-detail feature, a space attention mechanism is used to extract attention features from the central body feature and the edge-detail feature, to obtain a facial nerve segmentation image. The method improves the precision and speed of automatic facial nerve segmentation, and meets the needs of preoperative path planning for robotic cochlear implantation.

公开(公告)号：US20240201063A1

公开(公告)日：2024-06-20

申请号：US18592400

申请日：2024-02-29

Applicant: Ventana Medical Systems, Inc.

Inventor： Joerg Bredno ,  Auranuch Lorsakul

IPC: G01N15/0227 ,  G01N15/14 ,  G01N15/1433 ,  G06F18/232 ,  G06K1/00 ,  G06V10/25 ,  G06V10/762 ,  G06V20/69 ,  G16H10/40 ,  G16H30/40 ,  G16H50/70

CPC classification number: G01N15/0227 ,  G01N15/1433 ,  G06F18/232 ,  G06K1/00 ,  G06V10/25 ,  G06V10/763 ,  G06V20/695 ,  G16H10/40 ,  G16H30/40 ,  G16H50/70 ,  G01N2015/1497 ,  G06V2201/03

Abstract: The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and/or intensities in the input image, wherein each sub-region represents biologically meaningful data.

公开(公告)号：US20240194325A1

公开(公告)日：2024-06-13

申请号：US18586727

申请日：2024-02-26

Applicant: Blackford Analysis Ltd.

Inventor： Robert John TWEEDIE ,  Paul HENDERSON ,  Keith HOUSTON

IPC: G16H30/20 ,  G06F16/14 ,  G06F40/40 ,  G16H10/20 ,  G16H10/60 ,  G16H30/00 ,  G16H30/40 ,  G16H40/00 ,  G16H80/00 ,  H04L67/12

CPC classification number: G16H30/20 ,  G06F16/14 ,  G06F40/40 ,  G16H10/20 ,  G16H30/00 ,  G16H30/40 ,  G16H40/00 ,  H04L67/12 ,  G06T2210/41 ,  G06V2201/03 ,  G16H10/60 ,  G16H80/00

Abstract: A system and method for processing a plurality of medical images using a plurality of clinical applications. A current study is received by a first server, the current study having first image series metadata. The first server can determine, based on several different techniques, that the current study is in progress. The system and method generates an assembled study set comprising the current study that is processed using a clinical application, the current study having a first series and a second series.

公开(公告)号：US12009105B2

公开(公告)日：2024-06-11

申请号：US17197060

申请日：2021-03-10

Applicant: FUJIFILM Corporation

Inventor： Masaaki Oosake

IPC: G06V10/25 ,  A61B1/00 ,  G06F18/213 ,  G06F18/214 ,  G06N3/08 ,  G06T7/00 ,  G06V10/75 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G16H30/20 ,  G16H50/70

CPC classification number: G16H50/70 ,  A61B1/000094 ,  A61B1/000096 ,  G06F18/213 ,  G06F18/2148 ,  G06N3/08 ,  G06T7/0012 ,  G06V10/25 ,  G06V10/758 ,  G06V10/764 ,  G06V10/7747 ,  G06V10/82 ,  G16H30/20 ,  G06T2207/10016 ,  G06T2207/10068 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30096 ,  G06V2201/03

Abstract: There are provided a learning apparatus and a learning method that can facilitate creation of teaching data and prevent overtraining. A learning apparatus (10) includes a first database that stores a first image set in which a first image for learning and coordinate information for identifying a region larger than a region of interest included in the first image are associated with each other, and a second database that stores a second image set in which a second image for learning and second mask data for identifying a region of interest included in the second image are associated with each other. In a case of using the first image set to update a parameter of a CNN (32) (in a case of performing learning), a mask data creation unit (38) creates first mask data on the basis of the coordinate information for identifying the region larger than the region of interest. The first image and the second image are used as input images for the CNN (32), and the first mask data and the second mask data are used as teaching data to update the parameter of the CNN (32).

公开(公告)号：US20240185520A1

公开(公告)日：2024-06-06

申请号：US18574472

申请日：2022-06-13

Applicant: MEDIT CORP.

Inventor： Sung Hoon LEE ,  Dong Hoon LEE

IPC: G06T17/00 ,  A61C13/34 ,  G06T7/00 ,  G06T7/11 ,  G06T7/33 ,  G06T7/73 ,  G06V20/50 ,  G06V20/64

CPC classification number: G06T17/00 ,  A61C13/34 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/344 ,  G06T7/75 ,  G06V20/50 ,  G06V20/64 ,  G06T2200/04 ,  G06T2207/20021 ,  G06T2207/30036 ,  G06T2210/41 ,  G06V2201/03

Abstract: Provided are an intraoral image processing method and an intraoral image processing device. In detail, an intraoral image processing method according to an embodiment may include obtaining three-dimensional (3D) intraoral data on an oral cavity including teeth and a gingiva, and generating an outer surface (eggshell) of a target tooth that is a subject of a prosthesis from among the teeth included in the 3D intraoral data, wherein the generating of the outer surface includes automatically generating a portion corresponding to a void area between the target tooth and at least one adjacent tooth adjacent to the target tooth.

公开(公告)号：US20240169699A1

公开(公告)日：2024-05-23

申请号：US18056286

申请日：2022-11-17

Applicant: Siemens Healthcare GmbH

Inventor： Andrei Bogdan Gheorghita ,  Athira Jane Jacob ,  Lucian Mihai Itu ,  Puneet Sharma

IPC: G06V10/774 ,  G06T7/00

CPC classification number: G06V10/774 ,  G06T7/0012 ,  G06T2207/10088 ,  G06T2207/30048 ,  G06V2201/03

Abstract: CMR imaging is synthesized, and/or machine learning for a CMR imaging task uses synthetic sample generation. A machine-learned model generates synthetic samples. For example, the machine-learned model generates the synthetic samples in response to input of values for two or more parameters from the group of electrocardiogram (ECG), an indication of image style, a number of slices, a pathology, a measure of heart function, sample image, and/or an indication of slice position relative to anatomy. The indication of image style may be in the form of a latent representation, which may be used as the only input or one of multiple inputs. These inputs provide for better control over generation of synthetic samples, providing for greater variance and breadth of samples then used to machine train for a CMR task.

公开(公告)号：US11989885B2

公开(公告)日：2024-05-21

申请号：US17260855

申请日：2018-09-18

Applicant: HONOR DEVICE CO., LTD.

Inventor： Zhizhi Guo ,  Henghui Lu ,  Wenmei Gao

IPC: G06T7/11 ,  G06F18/241 ,  G06T7/00 ,  G06T7/514 ,  G06V10/56 ,  G06V40/10 ,  G06V40/16

CPC classification number: G06T7/11 ,  G06F18/241 ,  G06T7/0012 ,  G06T7/514 ,  G06V10/56 ,  G06V40/10 ,  G06V40/171 ,  G06T2207/10024 ,  G06T2207/30088 ,  G06V2201/03

Abstract: A speckle detection method includes: obtaining a to-be-detected image; converting the to-be-detected image into Lab color space to obtain a Lab image; extracting a speckle feature from the Lab image to obtain a speckle feature image, where the speckle feature image includes a skin speckle feature and a subcutaneous speckle feature; and determining a skin speckle and a subcutaneous speckle in the speckle feature image.

公开(公告)号：US20240161485A1

公开(公告)日：2024-05-16

申请号：US18423572

申请日：2024-01-26

Applicant: VENTANA MEDICAL SYSTEMS, INC.

Inventor： Yao Nie ,  Safoora Yousefi

IPC: G06V10/82 ,  G06F18/214 ,  G06F18/231 ,  G06F18/243 ,  G06V10/25 ,  G06V10/44 ,  G06V10/764 ,  G06V20/69

CPC classification number: G06V10/82 ,  G06F18/2155 ,  G06F18/231 ,  G06F18/243 ,  G06V10/25 ,  G06V10/454 ,  G06V10/764 ,  G06V20/695 ,  G06V20/698 ,  G06V2201/03

Abstract: The present disclosure relates to automated systems and methods adapted to quickly and accurately train a neural network to detect and/or classify cells and/or nuclei. The present disclosure also relates to automated systems and methods for using a trained cell detection and classification engine, such as one including a neural network, to classify cells within an unlabeled image.