title: Automatic analysis of trabecular bone structure from knee MRI.
abstract: We investigated the feasibility of quantifying osteoarthritis (OA) by analysis of the trabecular bone structure in low-field knee MRI. Generic texture features were extracted from the images and subsequently selected by sequential floating forward selection (SFFS), following a fully automatic, uncommitted machine-learning based framework. Six different classifiers were evaluated in cross-validation schemes and the results showed that the presence of OA can be quantified by a bone structure marker. The performance of the developed marker reached a generalization area-under-the-ROC (AUC) of 0.82, which is higher than the established cartilage markers known to relate to the OA diagnosis.
authors.affiliation: University of Copenhagen, 2100 Copenhagen, Denmark. jm@biomediq.com
authors.forename: Joselene
authors.initials: J
authors.lastname: Marques
authors.forename: Rabia
authors.initials: R
authors.lastname: Granlund
authors.forename: Martin
authors.initials: M
authors.lastname: Lillholm
authors.forename: Paola C
authors.initials: PC
authors.lastname: Pettersen
authors.forename: Erik B
authors.initials: EB
authors.lastname: Dam
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D015415:Biomarkers; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007717:Knee / Q000033:anatomy & histology* / Q000473:pathology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D020370:Osteoarthritis, Knee / Q000175:diagnosis / Q000473:pathology; D013977:Tibia / Q000473:pathology*
chemical_list: D015415:Biomarkers