Panfilov, E, Tiulpin, A, Nieminen, MT, Saarakkala, S, Casula, V. Deep learning-based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues: data from the Osteoarthritis Initiative. J Orthop Res. 2022; 40: 1113- 1124. https://doi.org/10.1002/jor.25150
Deep learning-based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues : data from the Osteoarthritis Initiative
|Author:||Panfilov, Egor1; Tiulpin, Aleksei1,2,3; Nieminen, Miika T.1,2;|
1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
2Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
3Ailean Technologies Oy, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022030722232
John Wiley & Sons,
|Publish Date:|| 2022-03-08
Morphological changes in knee cartilage subregions are valuable imaging-based biomarkers for understanding progression of osteoarthritis, and they are typically detected from magnetic resonance imaging (MRI). So far, accurate segmentation of cartilage has been done manually. Deep learning approaches show high promise in automating the task; however, they lack clinically relevant evaluation. We introduce a fully automatic method for segmentation and subregional assessment of articular cartilage, and evaluate its predictive power in context of radiographic osteoarthritis progression. Two data sets of 3D double-echo steady-state (DESS) MRI derived from the Osteoarthritis Initiative were used: first, n = 88; second, n = 600, 0-/12-/24-month visits. Our method performed deep learning-based segmentation of knee cartilage tissues, their subregional division via multi-atlas registration, and extraction of subregional volume and thickness. The segmentation model was developed and assessed on the first data set. Subsequently, on the second data set, the morphological measurements from our and the prior methods were analyzed in correlation and agreement, and, eventually, by their discriminative power of radiographic osteoarthritis progression over 12 and 24 months, retrospectively. The segmentation model showed very high correlation (r > 0.934) and agreement (mean difference < 116 mm³) in volumetric measurements with the reference segmentations. Comparison of our and manual segmentation methods yielded r = 0.845–0.973 and mean differences = 262–501 mm³ for weight-bearing cartilage volume, and r = 0.770–0.962 and mean differences = 0.513–1.138 mm for subregional cartilage thickness. With regard to osteoarthritis progression, our method found most of the significant associations identified using the manual segmentation method, for both 12- and 24-month subregional cartilage changes. The method may be effectively applied in osteoarthritis progression studies to extract cartilage-related imaging biomarkers.
Journal of orthopaedic research
|Pages:||1113 - 1124|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
217 Medical engineering
113 Computer and information sciences
3121 General medicine, internal medicine and other clinical medicine
The authors would like to acknowledge the following funding sources: strategic funding of University of Oulu (Infotech Oulu), Sigrid Juselius Foundation, KAUTE Foundation, and Jane and Aatos Erkko Foundation, Finland. Additionally, the authors gratefully acknowledge Santeri Rytky for his insight and scientific discussion. The OAI is a public‐private partnership comprised of five contracts (N01‐AR‐2‐2258; N01‐AR‐2‐2259; N01‐AR‐2‐2260; N01‐AR‐2‐2261; N01‐AR‐2‐2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. This manuscript was prepared using an OAI public use data set and does not necessarily reflect the opinions or views of the OAI investigators, the NIH, or the private funding partners.
© 2021 The Authors. Journal of Orthopaedic Research ® published by Wiley Periodicals LLC on behalf of Orthopaedic Research Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.