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：US20240153644A1

公开(公告)日：2024-05-09

申请号：US18407286

申请日：2024-01-08

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 ,  G06T15/06 ,  G06T17/20 ,  G06V10/26 ,  G06V10/764 ,  G06V20/70 ,  G16H30/40

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：US20220270762A1

公开(公告)日：2022-08-25

申请号：US17662802

申请日：2022-05-10

Applicant: Axial Medical Printing Limited

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

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

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.