A Paproki1, C Engstrom2, S S Chandra3, A Neubert4, J Fripp5, S Crozier6. 1. The Australian e-Health Research Centre, CSIRO Computational Informatics, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD 4027, Australia. Electronic address: anthony.paproki@uqconnect.edu.au. 2. School of Human Movement Studies, The University of Queensland, St Lucia, QLD 4072, Australia. Electronic address: c.engstrom@uq.edu.au. 3. The Australian e-Health Research Centre, CSIRO Computational Informatics, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia. Electronic address: shekhar.chandra@uq.edu.au. 4. The Australian e-Health Research Centre, CSIRO Computational Informatics, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD 4027, Australia. Electronic address: ales.neubert@uqconnect.edu.au. 5. The Australian e-Health Research Centre, CSIRO Computational Informatics, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia. Electronic address: jurgen.fripp@csiro.au. 6. School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD 4027, Australia. Electronic address: stuart@itee.uq.edu.au.
Abstract
OBJECTIVE: To validate an automatic scheme for the segmentation and quantitative analysis of the medial meniscus (MM) and lateral meniscus (LM) in magnetic resonance (MR) images of the knee. METHOD: We analysed sagittal water-excited double-echo steady-state MR images of the knee from a subset of the Osteoarthritis Initiative (OAI) cohort. The MM and LM were automatically segmented in the MR images based on a deformable model approach. Quantitative parameters including volume, subluxation and tibial-coverage were automatically calculated for comparison (Wilcoxon tests) between knees with variable radiographic osteoarthritis (rOA), medial and lateral joint space narrowing (mJSN, lJSN) and pain. Automatic segmentations and estimated parameters were evaluated for accuracy using manual delineations of the menisci in 88 pathological knee MR examinations at baseline and 12 months time-points. RESULTS: The median (95% confidence-interval (CI)) Dice similarity index (DSI) (2 ∗|Auto ∩ Manual|/(|Auto|+|Manual|)∗ 100) between manual and automated segmentations for the MM and LM volumes were 78.3% (75.0-78.7), 83.9% (82.1-83.9) at baseline and 75.3% (72.8-76.9), 83.0% (81.6-83.5) at 12 months. Pearson coefficients between automatic and manual segmentation parameters ranged from r = 0.70 to r = 0.92. MM in rOA/mJSN knees had significantly greater subluxation and smaller tibial-coverage than no-rOA/no-mJSN knees. LM in rOA knees had significantly greater volumes and tibial-coverage than no-rOA knees. CONCLUSION: Our automated method successfully segmented the menisci in normal and osteoarthritic knee MR images and detected meaningful morphological differences with respect to rOA and joint space narrowing (JSN). Our approach will facilitate analyses of the menisci in prospective MR cohorts such as the OAI for investigations into pathophysiological changes occurring in early osteoarthritis (OA) development.
OBJECTIVE: To validate an automatic scheme for the segmentation and quantitative analysis of the medial meniscus (MM) and lateral meniscus (LM) in magnetic resonance (MR) images of the knee. METHOD: We analysed sagittal water-excited double-echo steady-state MR images of the knee from a subset of the Osteoarthritis Initiative (OAI) cohort. The MM and LM were automatically segmented in the MR images based on a deformable model approach. Quantitative parameters including volume, subluxation and tibial-coverage were automatically calculated for comparison (Wilcoxon tests) between knees with variable radiographic osteoarthritis (rOA), medial and lateral joint space narrowing (mJSN, lJSN) and pain. Automatic segmentations and estimated parameters were evaluated for accuracy using manual delineations of the menisci in 88 pathological knee MR examinations at baseline and 12 months time-points. RESULTS: The median (95% confidence-interval (CI)) Dice similarity index (DSI) (2 ∗|Auto ∩ Manual|/(|Auto|+|Manual|)∗ 100) between manual and automated segmentations for the MM and LM volumes were 78.3% (75.0-78.7), 83.9% (82.1-83.9) at baseline and 75.3% (72.8-76.9), 83.0% (81.6-83.5) at 12 months. Pearson coefficients between automatic and manual segmentation parameters ranged from r = 0.70 to r = 0.92. MM in rOA/mJSN knees had significantly greater subluxation and smaller tibial-coverage than no-rOA/no-mJSN knees. LM in rOA knees had significantly greater volumes and tibial-coverage than no-rOA knees. CONCLUSION: Our automated method successfully segmented the menisci in normal and osteoarthritic knee MR images and detected meaningful morphological differences with respect to rOA and joint space narrowing (JSN). Our approach will facilitate analyses of the menisci in prospective MR cohorts such as the OAI for investigations into pathophysiological changes occurring in early osteoarthritis (OA) development.
Authors: Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana Journal: Acad Radiol Date: 2019-08-10 Impact factor: 3.173
Authors: Michal Byra; Mei Wu; Xiaodong Zhang; Hyungseok Jang; Ya-Jun Ma; Eric Y Chang; Sameer Shah; Jiang Du Journal: Magn Reson Med Date: 2019-09-19 Impact factor: 4.668
Authors: Dong Sun; Jan Neumann; Gabby B Joseph; Sarah Foreman; Michael C Nevitt; Charles E McCulloch; Xiaoming Li; Thomas M Link Journal: Eur Radiol Date: 2019-01-07 Impact factor: 5.315