Oral Presentation QMSKI Conference 2024

Automated processing for cartilage and bone assessment of MRI with metal artifacts and post-surgical tunnels in patients following ACLR (#246)

Jusuk Lee 1 , Adam G Culvenor 2 , Rupsa Bhattacharjee 1 , Misung Han 1 , Joshua Johnson 1 , Valentina Pedoia 1 , Sharmila Majumdar 1 , Kay M Crossley 2 , Richard B Souza 1 3
  1. Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States
  2. La Trobe Sport and Exercise Medicine Research Centre, La Trobe University, Bundoora, Victoria, Australia
  3. Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, California, United States
Publish consent withheld
  1. Culvenor, A. G. et al. SUpervised exercise-therapy and Patient Education Rehabilitation (SUPER) versus minimal intervention for young adults at risk of knee osteoarthritis after ACL reconstruction: SUPER-Knee randomised controlled trial protocol. BMJ Open 13, e068279 (2023).
  2. Isensee, F. et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18, 203–211 (2021).
  3. Iriondo, C. et al. Towards understanding mechanistic subgroups of osteoarthritis: 8‐year cartilage thickness trajectory analysis. Journal Orthopaedic Research 39, 1305–1317 (2021).
  4. Panfilov, E. et al. Deep learning‐based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues: Data from the Osteoarthritis Initiative. Journal Orthopaedic Research 40, 1113–1124 (2022).