title: Volumetric quantitative characterization of human patellar cartilage with topological and geometrical features on phase-contrast X-ray computed tomography.
abstract: Phase-contrast X-ray computed tomography (PCI-CT) has attracted significant interest in recent years for its ability to provide significantly improved image contrast in low absorbing materials such as soft biological tissue. In the research context of cartilage imaging, previous studies have demonstrated the ability of PCI-CT to visualize structural details of human patellar cartilage matrix and capture changes to chondrocyte organization induced by osteoarthritis. This study evaluates the use of geometrical and topological features for volumetric characterization of such chondrocyte patterns in the presence (or absence) of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and topological features derived from Minkowski Functionals were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These features were subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). Our results show that the classification performance of SIM-derived geometrical features (AUC: 0.90 ± 0.09) is significantly better than Minkowski Functionals volume (AUC: 0.54 ± 0.02), surface (AUC: 0.72 ± 0.06), mean breadth (AUC: 0.74 ± 0.06) and Euler characteristic (AUC: 0.78 ± 0.04) (p < 10(-4)). These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as diagnostic imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies.
authors.affiliation: Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, Rochester, NY, USA. mbnagaraj@gmail.com.
authors.forename: Mahesh B
authors.initials: MB
authors.lastname: Nagarajan
authors.affiliation: Faculty of Medicine & Institute of Clinical Radiology, Ludwig Maximilians University, 80336, Munich, Germany.|Department of Physics, Ludwig Maximilians University, 85748, Munich, Germany.|European Synchrotron Radiation Facility, 38000, Grenoble, France.
authors.forename: Paola
authors.initials: P
authors.lastname: Coan
authors.affiliation: Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, Rochester, NY, USA.
authors.forename: Markus B
authors.initials: MB
authors.lastname: Huber
authors.affiliation: Department of Physics, Ludwig Maximilians University, 85748, Munich, Germany.|European Synchrotron Radiation Facility, 38000, Grenoble, France.
authors.forename: Paul C
authors.initials: PC
authors.lastname: Diemoz
authors.affiliation: Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, Rochester, NY, USA.|Faculty of Medicine & Institute of Clinical Radiology, Ludwig Maximilians University, 80336, Munich, Germany.
authors.forename: Axel
authors.initials: A
authors.lastname: Wismüller
mesh_terms: D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D020370:Osteoarthritis, Knee / Q000000981:diagnostic imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Minkowski functionals; Osteoarthritis; Phase-contrast imaging; Scaling index method; Support vector regression; Volumetric features