US 12,277,712 B2
Method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images
Niall Haslam, Belfast (GB); Lorenzo Trojan, Belfast (GB); and Daniel Crawford, Belfast (GB)
Assigned to Axial Medical Printing Limited, Belfast (GB)
Filed by Axial Medical Printing Limited, Belfast (GB)
Filed on Mar. 4, 2024, as Appl. No. 18/595,213.
Application 18/595,213 is a continuation of application No. 18/359,821, filed on Jul. 26, 2023, granted, now 11,922,631.
Application 18/359,821 is a continuation of application No. 18/150,112, filed on Jan. 4, 2023, granted, now 11,715,210, issued on Aug. 1, 2023.
Application 18/150,112 is a continuation of application No. 17/656,189, filed on Mar. 23, 2022, granted, now 11,551,420, issued on Jan. 10, 2023.
Application 17/656,189 is a continuation of application No. 17/491,183, filed on Sep. 30, 2021, granted, now 11,288,865, issued on Mar. 29, 2022.
Application 17/491,183 is a continuation of application No. 17/115,102, filed on Dec. 8, 2020, granted, now 11,138,790, issued on Oct. 5, 2021.
Application 17/115,102 is a continuation of application No. 16/341,554, granted, now 11,497,557, issued on Nov. 15, 2022, previously published as PCT/GB2017/053125, filed on Oct. 16, 2017.
Claims priority of application No. 1617507 (GB), filed on Oct. 14, 2016.
Prior Publication US 2024/0202927 A1, Jun. 20, 2024
Int. Cl. G06K 9/00 (2022.01); G06F 18/24 (2023.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 17/20 (2006.01); G06V 10/26 (2022.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01)
CPC G06T 7/11 (2017.01) [G06F 18/24 (2023.01); G06T 7/0014 (2013.01); G06T 17/20 (2013.01); G06V 10/26 (2022.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); G06T 2200/08 (2013.01); G06T 2207/30004 (2013.01); G06V 2201/03 (2022.01)] 30 Claims
OG exemplary drawing
 
1. A computer-implemented method for defining patient specific anatomical features from medical images, the computer-implemented method comprising:
receiving medical images and information on bone and/or soft tissue of a patient;
applying the information as an input to a segmentation algorithm to automatically process the medical images to assign a label for each pixel of the medical images based at least partially on the bone and/or soft tissue, the segmentation algorithm including a convolutional Neural Network method trained from a database of existing medical images that have been labelled and a medical imaging ontology;
accessing a database of medical image anatomical features;
using an anatomical feature identification algorithm to probabilistically match the labeled pixels of the medical images against the database of medical image anatomical features to generate segmentation data that defines one or more patient specific anatomical features based on the labeled pixels of the medical images; and
generating a 3D model of the one or more patient specific anatomical features comprising the bone and/or soft tissue using the generated segmentation data.