IP Library › Granted Patent US 12,444,506
Granted Patent B2
US 12,444,506 · App. 18/959,412 · Granted Oct 14, 2025

Systems and methods for automated segmentation of patient specific anatomies for pathology specific measurements

Inventors: Rory Hanratty (Belfast, GB); Daniel Crawford (Belfast, GB); Martin Jaere (Belfast, GB); Luis Trindade (Craigavon, GB); Thomas Schwarz (Sussex, GB); Adam Harpur (Bangor, GB)
Assignee: Axial Medical Printing Limited
G16H50/50G06T7/0016G06T7/62G06T15/04G06T15/06G06T17/20G06V10/26G06V10/764G06V20/70G16H30/40G06T2210/21G06T2210/41G06V2201/03
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,444,506
App. No.
18/959,412
Filed
Nov 25, 2024
Granted
Oct 14, 2025
Kind
B2
Examiner
WU, SING-WAI
Art Unit
2611
USPC
345/423
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 landmark detection algorithm leveraging Deep Reinforcement Learning (DRL) techniques to automatically locate one or more anatomical landmarks associated with the patient specific anatomical feature within the medical images. A 3D surface mesh model may be generated representing the patient specific anatomical features including the located one or more anatomical landmarks. The located one or more anatomical landmarks may be used to guide placement of a 3D model of a medical device that may be fused with the 3D surface mesh model to generate a patient specific 3D model of the medical device.

Claims (48)

1. A system for multi-schema analysis of patient specific anatomical features from medical images, the system comprising a server and configured to:

receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology;

automatically segment the medical images to identify one or more patient specific anatomical features within the medical images;

extract an isolated patient specific anatomical feature comprising the selected pathology from the one or more patient specific anatomical features based on the metadata;

generate an isolated 3 D surface mesh model defining a surface of the isolated patient specific anatomical feature; and

provide a medical device to be used to treat the selected pathology based on physiological parameters of the isolated patient specific anatomical feature.

2. The system of claim 1 , wherein the physiological parameters of the isolated anatomical feature comprise a size of the selected pathology, and wherein the medical device provided comprises a specific sized medical device for treating the selected pathology.

3. The system of claim 1 , wherein the system is configured to generate physiological information associated with the selected pathology for the isolated 3 D surface mesh model.

4. The system of claim 3 , wherein the physiological information comprises a volume, cross-sectional area, diameter, centerline, surface, density, thickness, tortuosity, fracture size and location, blood clots, occlusions, and/or rate of growth over time of the anatomical feature and/or corresponding landmark.

5. The system of claim 1 , wherein the system is configured to automatically segment the medical images to identify the one or more patient specific anatomical features via a segmentation algorithm programmed to label pixels of the medical images and an anatomical feature identification algorithm programmed to classify the one or more patient specific anatomical features within the medical images based on the labeled pixels.

6. The system of claim 1 , wherein the medical device is selected for fixation to bone and to provide guidance for cutting planes.

7. The system of claim 1 , wherein the medical device is selected for fixation to bone and to provide drilling trajectories within the bone.

8. The system of claim 1 , wherein the medical device is selected based on a surgical procedure to be performed on the isolated patient specific anatomical feature to treat the selected pathology.

9. The system of claim 8 , wherein the surgical procedure comprises placement of bone fragments, tools, implants, holes, and/or screws, cutting, and/or bone realignment.

10. The system of claim 1 , wherein the medical device is selectable from a hospital inventory of available medical devices.

11. The system of claim 1 , wherein the system is configured to:

access a medical device database having knowledge of various medical devices, the knowledge comprising function and/or specifications of the various medical devices,

wherein the medical device is selectable from a list of medical devices extracted from the medical device database.

12. The system of claim 1 , wherein the system is configured to:

access a surgical implement database having knowledge of pathology-specific treatment options,

wherein the medical device is selectable from a list of medical devices extracted from the surgical implement database.

13. The system of claim 1 , wherein the system is configured to provide the medical device by providing a 3D digital model of the medical device specific to the physiological parameters of the isolated patient specific anatomical feature.

14. The system of claim 13 , wherein the system is configured to:

receive patient demographic data,

wherein the 3D digital model of the medical device is provided based at least partially on the patient demographic data.

15. The system of claim 13 , wherein the system is configured to cause a display to display the 3D digital model of the medical device.

16. The system of claim 15 , wherein the display comprises a graphical user interface, and wherein the system is configured to permit a user to move the 3D digital model of the medical device relative to the isolated patient specific anatomical feature via the graphical user interface to position the 3D digital model of the medical device at a target location relative to the isolated patient specific anatomical feature.

17. The system of claim 16 , wherein the system is configured to display one or more anatomical landmarks associated with the isolated patient specific anatomical feature, the one or more anatomical landmarks configured to serve as a guide for positioning of the 3D digital model of the medical device at the target location.

18. The system of claim 16 , wherein the system is configured to fuse the 3D digital model of the medical device with the isolated patient specific anatomical feature such that a fitting surface of the 3D digital model matches a surface contour of the isolated patient specific anatomical feature at the target location.

19. The system of claim 1 , wherein the system is further configured to cause the 3D digital model to be 3D printed to generate a physical, patient specific medical device.

20. The system of claim 19 , wherein the physical, patient-specific medical device is a cutting guide, pin guide, occlusion device, mitral valve implant, aortic valve implant, stent, coil, clip, fusion plate, or joint replacement implant.

21. A method for multi-schema analysis of patient specific anatomical features from medical images, the method comprising:

receiving medical images of a patient and metadata associated with the medical images indicative of a selected pathology;

automatically segmenting the medical images to identify one or more patient specific anatomical features within the medical images;

extracting an isolated patient specific anatomical feature comprising the selected pathology from the one or more patient specific anatomical features based on the metadata;

generating an isolated 3 D surface mesh model defining a surface of the isolated patient specific anatomical feature; and

providing a medical device to be used to treat the selected pathology based on physiological parameters of the isolated patient specific anatomical feature.

22. The method of claim 21 , wherein the physiological parameters of the isolated anatomical feature comprise a size of the selected pathology, and wherein the medical device provided comprises a specific sized medical device for treating the selected pathology.

23. The method of claim 21 , wherein automatically segmenting the medical images to identify the one or more patient specific anatomical features comprises executing a segmentation algorithm programmed to label pixels of the medical images and an anatomical feature identification algorithm programmed to classify the one or more patient specific anatomical features within the medical images based on the labeled pixels.

24. The method of claim 21 , wherein the medical device is selected for fixation to bone and to provide guidance for cutting planes, selected for fixation to bone and to provide drilling trajectories within the bone, and/or selected based on a surgical procedure to be performed on the isolated patient specific anatomical feature to treat the selected pathology.

25. The method of claim 21 , wherein providing the medical device comprises providing a 3 D digital model of the medical device specific to the physiological parameters of the isolated patient specific anatomical feature.

26. The method of claim 25 , further comprising:

causing a graphical user interface to display the 3D digital model of the medical device; and

permitting, via the graphical user interface, a user to move the 3D digital model of the medical device relative to the isolated patient specific anatomical feature to position the 3D digital model of the medical device at a target location relative to the isolated patient specific anatomical feature.

27. The method of claim 26 , further comprising causing the graphical user interface to display one or more anatomical landmarks associated with the isolated patient specific anatomical feature, the one or more anatomical landmarks configured to serve as a guide for positioning of the 3D digital model of the medical device at the target location.

28. The method of claim 25 , further comprising fusing the 3D digital model of the medical device with the isolated patient specific anatomical feature such that a fitting surface of the 3D digital model matches a surface contour of the isolated patient specific anatomical feature at the target location.

29. The method of claim 25 , further comprising 3D printing the 3D digital model to generate a physical, patient specific medical device.

30. The method of claim 29 , wherein the physical, patient-specific medical device is a cutting guide, pin guide, occlusion device, mitral valve implant, aortic valve implant, stent, coil, clip, fusion plate, or joint replacement implant.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 4, 2025
From: HANRATTY, RORY; CRAWFORD, DANIEL; JAERE, MARTIN; TRINDADE, LUIS; SCHWARZ, THOMAS; HARPUR, ADAM
To: AXIAL MEDICAL PRINTING LIMITED
Reel/Frame 071927/0888 →
Priority Claims (1)
GB 2101908.8 · Feb 11, 2021 · national
Continuity (5)
Continuation In Part 18407286 · Jan 8, 2024
Continuation 18131859 · Apr 6, 2023
Continuation 17662802 · May 10, 2022
Continuation PCTIB2022051216 · Feb 10, 2022
Related Publication 20250095864A1 · Mar 20, 2025
References Cited (112)
US 5859891A · Hibbard · 1999 [cited by applicant]
US 9437119B1 · Bernal · 2016 [cited by applicant]
US 9646411B2 · Lee · 2017 [cited by applicant]
US 10032281B1 · Ghesu et al. · 2018 [cited by applicant]
US 10409235B2 · Zhou et al. · 2019 [cited by applicant]
US 10946586B2 · Casey et al. · 2021 [cited by applicant]
US 10981680B2 · Colson et al. · 2021 [cited by applicant]
US 11010800B2 · Norman · 2021 [cited by applicant]
US 11059228B2 · Elber et al. · 2021 [cited by applicant]
US 11138790B2 · Haslam et al. · 2021 [cited by applicant]
US 11288865B2 · Haslam et al. · 2022 [cited by applicant]
US 11436801B2 · Haslam et al. · 2022 [cited by applicant]
US 11497557B2 · Haslam et al. · 2022 [cited by applicant]
US 11551420B2 · Haslam et al. · 2023 [cited by applicant]
US 11626212B2 · Crawford et al. · 2023 [cited by applicant]
US 11715210B2 · Haslam et al. · 2023 [cited by applicant]
US 11869670B2 · Crawford et al. · 2024 [cited by applicant]
US 11922631B2 · Haslam et al. · 2024 [cited by applicant]
US 12020375B2 · Haslam et al. · 2024 [cited by applicant]
US 20090316975A1 · Kunz et al. · 2009 [cited by applicant]
US 20100156904A1 · Hartung · 2010 [cited by applicant]
US 20110038516A1 · Koehler et al. · 2011 [cited by applicant]
US 20110218428A1 · Westmoreland et al. · 2011 [cited by applicant]
US 20120059252A1 · Li et al. · 2012 [cited by applicant]
US 20120224755A1 · Wu · 2012 [cited by applicant]
US 20130002646A1 · Lin et al. · 2013 [cited by applicant]
US 20140328529A1 · Koceski et al. · 2014 [cited by applicant]
US 20140361453A1 · Triantafyllou · 2014 [cited by applicant]
US 20150089337A1 · Grady et al. · 2015 [cited by applicant]
US 20150169985A1 · Burger et al. · 2015 [cited by applicant]
US 20150231417A1 · Metcalf · 2015 [cited by examiner]
US 20150342537A1 · Taylor et al. · 2015 [cited by applicant]
US 20160086078A1 · Ji et al. · 2016 [cited by applicant]
US 20160110517A1 · Taylor · 2016 [cited by applicant]
US 20160300350A1 · Choi et al. · 2016 [cited by applicant]
US 20170007129A1 · Kaib et al. · 2017 [cited by applicant]
US 20170228505A1 · Allen et al. · 2017 [cited by applicant]
US 20170245821A1 · Itu et al. · 2017 [cited by applicant]
US 20170329930A1 · Fonte et al. · 2017 [cited by applicant]
US 20170333604A1 · Cohn · 2017 [cited by examiner]
US 20180028265A1 · Azevedo et al. · 2018 [cited by applicant]
US 20180092699A1 · Finley · 2018 [cited by applicant]
US 20180165867A1 · Kuhn et al. · 2018 [cited by applicant]
US 20180276815A1 · Xu et al. · 2018 [cited by applicant]
US 20180365835A1 · Yan et al. · 2018 [cited by applicant]
US 20190053855A1 · Siemionow et al. · 2019 [cited by applicant]
US 20190105009A1 · Siemionow et al. · 2019 [cited by applicant]
US 20190108635A1 · Hibbard et al. · 2019 [cited by applicant]
US 20190205606A1 · Zhou et al. · 2019 [cited by applicant]
US 20190251694A1 · Han et al. · 2019 [cited by applicant]
US 20190392942A1 · Sorenson et al. · 2019 [cited by applicant]
US 20200074637A1 · Wong · 2020 [cited by applicant]
US 20200367970A1 · Qiu et al. · 2020 [cited by applicant]
US 20200402647A1 · Domracheva et al. · 2020 [cited by applicant]
US 20210068714A1 · Crowley et al. · 2021 [cited by applicant]
US 20210074425A1 · Carter et al. · 2021 [cited by applicant]
US 20210097690A1 · Mostapha et al. · 2021 [cited by applicant]
US 20210110605A1 · Haslam et al. · 2021 [cited by applicant]
US 20210335041A1 · Haslam et al. · 2021 [cited by applicant]
US 20230281842A1 · Ribeiro et al. · 2023 [cited by applicant]
US 20240005504A1 · Ribeiro et al. · 2024 [cited by applicant]
CN 105051784A · 2015 [cited by applicant]
CN 105608728A · 2016 [cited by applicant]
CN 106373168A · 2017 [cited by applicant]
CN 106456125A · 2017 [cited by applicant]
CN 108601552A · 2018 [cited by applicant]
EP 3020537A1 · 2016 [cited by applicant]
WO WO2016161198A1 · 2016 [cited by applicant]
WO WO2018069736A1 · 2018 [cited by applicant]
WO WO2018222779A1 · 2018 [cited by applicant]
WO WO2020144483A1 · 2020 [cited by applicant]
WO WO2023096516A1 · 2023 [cited by applicant]
U.S. Appl. No. 16/341,554 / U.S. Pat. No. 11,497,557, filed Apr. 12, 2019 / Nov. 15, 2022. [cited by applicant]
U.S. Appl. No. 17/115,102 / U.S. Pat. No. 11,138,790, filed Dec. 8, 2020 / Oct. 5, 2021. [cited by applicant]
U.S. Appl. No. 17/372,087 / U.S. Pat. No. 11,436,801, filed Jul. 9, 2021 / Sep. 6, 2022. [cited by applicant]
U.S. Appl. No. 17/491,183 / U.S. Pat. No. 11,288,865, filed Sep. 30, 2021 / Mar. 26, 2022. [cited by applicant]
U.S. Appl. No. 17/656,189 / U.S. Pat. No. 11,551,420, filed Mar. 23, 2022 / Jan. 10, 2023. [cited by applicant]
U.S. Appl. No. 17/662,802 / U.S. Pat. No. 11,626,212, filed May 10, 2022 / Apr. 11, 2023. [cited by applicant]
U.S. Appl. No. 17/929,702 / U.S. Pat. No. 12,020,375, filed Sep. 4, 2022 / Jun. 25, 2024. [cited by applicant]
U.S. Appl. No. 18/131,859 / U.S. Pat. No. 11,869,670, filed Apr. 6, 2023 / Jan. 9, 2024. [cited by applicant]
U.S. Appl. No. 18/150,112 / U.S. Pat. No. 11,715,210, filed Jan. 4, 2023 / Aug. 1, 2023. [cited by applicant]
U.S. Appl. No. 18/359,821 / U.S. Pat. No. 11,922,631, filed Jul. 26, 2023 / Mar. 5, 2024. [cited by applicant]
U.S. Appl. No. 18/407,286 / U.S. Pat. No. 12,154,691, filed Jan. 8, 2024 / Nov. 26, 2024. [cited by applicant]
U.S. Appl. No. 18/595,213, filed Mar. 4, 2024. [cited by applicant]
U.S. Appl. No. 18/751,032, filed Jun. 21, 2024. [cited by applicant]
Aisaeed et al., “A Novel Fast Otsu Digital Image Segmentation Method,” The International Arab Journal of Information Technology, vol. 13(4):427-433 (Jul. 2016). [cited by applicant]
Baghaie, et al., An Optimization Method For Slice Interpolation of Medical Images, arXiv preprint arXiv:1402.0936 (Feb. 2014). [cited by applicant]
Boulton, et al., Lessons from the National Hip Fracture Database, Orthopaedics and Trauma, 30(2):123-127 (Apr. 2016). [cited by applicant]
Brown, et al., Using Machine Learning for Sequence-Level Automated MRI Protocol Selection in Neuroradiology, Journal of the American Medical Informatics Association, 25(5):568-71 (May 2018). [cited by applicant]
Carvalho, et al., Estimating 3D lumen centerlines of carotid arteries in free-hand acquisition ultrasound, International Journal of Computer Assisted Radiology and Surgery, 7(2):207-215 (Mar. 2012). [cited by applicant]
Cui, et al., Brain MRI Segmentation with Patch-Based CNN Approach, Proceedings of the 35th Chinese Control Conference, Jul. 27-29, 2016, pp. 7026-7031. [cited by applicant]
Dou, et al., 3D Deeply Supervised Network For Automated Segmentation of Volumetric Medical Images, Medical Image Analysis, 41:40-54 (Oct. 2017). [cited by applicant]
Geremia, et al., Spatial Decision Forests for MS Lesion Segmentation in Multi-Channel Magnetic Resonance Images, NeuroImage, 57(2):378-390 (Jul. 2011). [cited by applicant]
Heckelman, et al., “Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency,” Scientific Reports, vol. 12:7825, https://doi.org/10.1038/s41598-022-11785-6 (2022). [cited by applicant]
International Search Report & Written Opinion dated Feb. 16, 2018 in Int'l PCT Patent Appl. Serial No. PCT/GB2017/053125 (0110). [cited by applicant]
International Search Report & Written Opinion dated May 12, 2022 in Int'l PCT Patent Appl. Serial No. PCT/IB2022/051216 (0310). [cited by applicant]
International Search Report & Written Opinion dated Jun. 25, 2020 in Int'l PCT Patent Appl. Serial No. PCT/GB2020/050063 (0210). [cited by applicant]
Laosai et al., Acute Leukemia Classification by Using SVM and K-Means Clustering, 2014 Proceedings of the International Electrical Engineering Congress (IEECON), pp. 1-4 (Mar. 19, 2014). [cited by applicant]
Lee, et al., Human Airway Measurement from CT Images. In Medical Imaging 2008: Computer-Aided Diagnosis, Proc. SPIE 6915:386-383 (Mar. 2008). [cited by applicant]
Litjens, et al., A Survey on Deep Learning in Medical Image Analysis, Medical Image Analysis, 42:60-88 (Dec. 2017). [cited by applicant]
Milletari, et al., V-Net: Fully Convolutional Neural networks For Volumetric Medical Image Segmentation, arXiv preprint arXiv: 1606.04797 (Jun. 2016). [cited by applicant]
Monteiro et al., “Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation,” arXiv preprint arXiv:1807.07464, (Jul. 2019). [cited by applicant]
Mortensen et al., “Semantic Segmentation of Mixed Crops Using Deep Convolutional Neural Network,” CIGr-AgEng Conference, pp. 26-29, (Jun. 2016). [cited by applicant]
Pinheiro et al., A new Level-Set-Based Protocol for Accurate Bone Segmentation from CT Imaging, IEEE Access, vol. 3:1894-1906 (Sep. 2015). [cited by applicant]
Richmond et al., “Mapping Stacked Decision Forests to Deep and Sparse Convolution Neural Networks for Semantic Segmentation,” arXiv, pp. 1-15 (Dec. 2015). [cited by applicant]
Rogowska, et al., Overview and Fundamentals of Medical Image Segmentation, Handbook of Medical Imaging, Processing and Analysis, pp. 69-85 (Oct. 2000). [cited by applicant]
Schmauss D., et al., “Three-Dimensional Printing in Cardiac Surgery and Interventional Cardiology: A Single-Centre Experience,” European Journal of Cardio-Thoracic Surgery, Aug. 26, 2014, vol. 47(6), pp. 1044-1052. [cited by applicant]
Tabrizi, et al., “Acetabular cartilage segmentation in CT arthrography based on a bone-normalized probabilistic atlas,” Int J Cars, vol. 10:433-446 (2015). [cited by applicant]
Yu, et al., 3D FractalNet: Dense Volumetric Segmentation for Cardiovascular MRI Volumes, In Reconstruction, Segmentation, and Analysis of Medical Images, pp. 103-110 (Oct. 2016). [cited by applicant]
Yu, et al., 3D FractaNet: dense volumetric segmentation for cardiovascular MRI volumes. In Reconstruction, Segmentation, and Analysis of Medical Images. First International Workshops, RAMBO 2016 and HVSMR 2016. Held in … [cited by applicant]
Zhou, et al., Deep convolutional neural network for segmentation of knee joint anatomy, Mag. Reson. Med., 80(6):2759-2770 (Dec. 2018). [cited by applicant]
Zhuang, J., “Laddernet: Multi-Path Networks based on U-Net For Medical Image Segmentation,” arXiv:preprint arXiv:1810.07810., pp. 1-4, (Oct. 2018). [cited by applicant]