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11.发明公开公开(公告)号：US20230410317A1
公开(公告)日：2023-12-21
申请号：US18359821
申请日：2023-07-26
Applicant: Axial Medical Printing Limited
Inventor： Niall HASLAM , Lorenzo TROJAN , Daniel CRAWFORD
CPC classification number: G06T7/11 , G06T17/20 , G06T7/0014 , G16H30/40 , G16H50/70 , G06V2201/03 , G06F18/24 , G06V10/26 , G06T2200/08 , G06T2207/30004 , G16H50/50
Abstract: There is provided a method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images. The 2D medical images are uploaded by an end-user via a Web Application and sent to a server. The server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique. The 3D printable model is 3D printed as a 3D physical model such that it represents a 1:1 scale of the patient specific anatomic feature. The method includes the step of automatically identifying the patient specific anatomic feature.
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12.发明公开公开(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.
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13.发明公开公开(公告)号：US20230141276A1
公开(公告)日：2023-05-11
申请号：US18150112
申请日：2023-01-04
Applicant: Axial Medical Printing Limited
Inventor： Niall HASLAM , Lorenzo TROJAN , Daniel CRAWFORD
CPC classification number: G06T7/11 , G06T17/20 , G06T7/0014 , G16H30/40 , G16H50/70 , G16H50/50 , G06F18/24 , G06V10/26 , G06T2200/08 , G06T2207/30004 , G06V2201/03
Abstract: There is provided a method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images. The 2D medical images are uploaded by an end-user via a Web Application and sent to a server. The server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique. The 3D printable model is 3D printed as a 3D physical model such that it represents a 1:1 scale of the patient specific anatomic feature. The method includes the step of automatically identifying the patient specific anatomic feature.
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14.发明申请公开(公告)号：US20210110605A1
公开(公告)日：2021-04-15
申请号：US17115102
申请日：2020-12-08
Applicant: AXIAL MEDICAL PRINTING LIMITED
Inventor： Niall HASLAM , Lorenzo TROJAN , Daniel CRAWFORD
Abstract: There is provided a method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images. The 2D medical images are uploaded by an end-user via a Web Application and sent to a server. The server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique. The 3D printable model is 3D printed as a 3D physical model such that it represents a 1:1 scale of the patient specific anatomic feature. The method includes the step of automatically identifying the patient specific anatomic feature.
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