Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
561-02-003 ISMRM Abstract

Multimodal MRI-Based Deep Learning for Automated Knee Cartilage Injury Classification

Primary: Musculoskeletal - Cartilage
Secondary: Physics & Engineering - Hybrid & Novel Systems Technology
561-02-003 · Classification and Analysis in the Body · Wednesday, 13 May, 9:15 AM–10:10 AM · Digital Posters Row B
Keywords: Radiomics Deep learning Knee cartilageinjury Automatic segmentation
Accepted
Zongbo Wang 1,2, Yanhui Liu3, Zhiwei Zhang4, Qi Ai1, Li Kaixin5, MENGZHU WANG5, Yang Song5, Esther Raithel6, Jinghong Yu2, Jing Shen7, Jianlin Wu8
1Graduate School, Tianjin Medical University, Tianjin, China
2Department of Radiology, The Second Affiliated Hospital, Inner Mongolia Medical University, Hohhot, China
3Department of Radiology, Hohhot First Hospital, Hohhot, China
4SingularityFlow Co. Ltd., Beijing, China
5MR Research Collaboration, Siemens Healthineers Ltd., Beijing, China
6Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
7Zhongshan Hospital, Dalian University, dalian, China
8Department of Radiology, Zhongshan Hospital, Dalian University, dalian, China
Presenting Author: Zongbo Wang

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. For knee cartilage segmentation with option CAN3D: Dai W, Woo B, Liu S, Marques M, Engstrom C, Greer P, Crozier S, Dowling J, Chandra S (2022). CAN3D: Fast 3D medical image segmentation via compact context aggregation. Medical Image Analysis 82: 102562, ISSN 1361-8415
2. For knee cartilage segmentation with option Robust or Robust Fixed: Fripp J, Crozier S, Warfield SK, Ourselin S (2010). Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE Trans Med Imaging 29(1): 55-64. doi: 10.1109/TMI.2009.2024743. PMID: 19520633. [doi] [pmid]
3. 3.For hip cartilage segmentation: Chandra SS, Xia Y, Engstrom C, Crozier S, Schwarz R, Fripp J (2014). Focused shape models for hip joint segmentation in 3D magnetic resonance images. Med Image Anal. 18(3): 567-78. doi: 10.1016/j.media.2014.02.002. PMID: 24614321. [doi] [pmid]
4. 4.For shoulder cartilage segmentation: Yang Z, Fripp J, Chandra SS, Neubert A, Xia Y, Strudwick M, Paproki A, Engstrom C, Crozier S (2015). Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images. Physics in medicine and biology 60(4): 1441-59. doi: 10.1088/0031-9155/60/4/1441. PMID: 25611124. [doi] [pmid]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/561-02-003