Open AccessA Coarse-to-Fine Framework for Automated Knee Bone and Cartilage Segmentation Data from the Osteoarthritis Initiative
Author(s) -
Yan Deng,
Lei You,
Yanfei Wang,
Xiaobo Zhou
Publication year - 2021
Publication title -
journal of digital imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.055
H-Index - 58
eISSN - 1618-727X
pISSN - 0897-1889
DOI - 10.1007/s10278-021-00464-z
Subject(s) - osteoarthritis , cartilage , segmentation , magnetic resonance imaging , medicine , meniscus , knee joint , radiology , computer science , artificial intelligence , pathology , surgery , anatomy , alternative medicine , incidence (geometry) , optics , physics
Knee osteoarthritis (OA) is a degenerative joint disease that is prevalent in advancing age. The pathology of OA disease is still unclear, and there are no effective interventions that can completely alter the OA disease process. Magnetic resonance (MR) image evaluation is sensitive for depicting early changes of knee OA, and therefore important for early clinical intervention for relieving the symptom. Automated cartilage segmentation based on MR images is a vital step in experimental longitudinal studies to follow-up the patients and prospectively define a new quantitative marker from OA progression. In this paper, we develop a deep learning-based coarse-to-fine approach for automated knee bone, cartilage, and meniscus segmentation with high computational efficiency. The proposed method is evaluated using two-fold cross-validation on 507 MR volumes (81,120 slices) with OA from the Osteoarthritis Initiative (OAI) 1 dataset. The mean dice similarity coefficients (DSCs) of femoral bone (FB), tibial bone (TB), femoral cartilage (FC), and tibial cartilage (TC) separately are 99.1%, 98.2%, 90.9%, and 85.8%. The time of segmenting each patient is 12 s, which is fast enough to be used in clinical practice. Our proposed approach may provide an automated toolkit to help computer-aided quantitative analyses of OA images.