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Computer-Aided Diagnosis for Phase-Contrast X-ray Computed Tomography: Quantitative Characterization of Human Patellar Cartilage with High-Dimensional Geometric Features

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Abstract

Phase-contrast computed tomography (PCI-CT) has shown tremendous potential as an imaging modality for visualizing human cartilage with high spatial resolution. Previous studies have demonstrated the ability of PCI-CT to visualize (1) structural details of the human patellar cartilage matrix and (2) changes to chondrocyte organization induced by osteoarthritis. This study investigates the use of high-dimensional geometric features in characterizing such chondrocyte patterns in the presence or absence of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and statistical features derived from gray-level co-occurrence matrices were extracted from 842 regions of interest (ROI) 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 ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic curve (AUC). SIM-derived geometrical features exhibited the best classification performance (AUC, 0.95 ± 0.06) and were most robust to changes in ROI size. These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated and non-subjective manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies.

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Acknowledgments

This research was funded in part by the National Institute of Health (NIH) Award R01-DA-034977, the Clinical and Translational Science Award 5-28527 within the Upstate New York Translational Research Network (UNYTRN) of the Clinical and Translational Science Institute (CTSI), University of Rochester, by the Center for Emerging and Innovative Sciences (CEIS), a NYSTAR-designated Center for Advanced Technology, and by the cluster of excellence "Munich-centre for Advanced Photonics"' (MAP), Munich, Germany. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Health. The authors would like to thank Dr. Emmanuel Brun for his assistance with the data sharing process, and Benjamin Mintz for his assistance in developing the annotation tool used in this study. Prof. Dr. Maximilian Reiser, FACR, FRCR of the Department of Radiology, Ludwig Maximilians University, is also acknowledged for his continued support.

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Authors and Affiliations

  1. Department of Biomedical Engineering, University of Rochester, 430 Elmwood Ave, Rochester, NY, 14627, USA

    Mahesh B. Nagarajan

  2. Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilians University, Munich, 80336, Germany

    Paola Coan, Christian Glaser & Axel Wismüller

  3. Faculty of Physics, Ludwig Maximilians University, Munich, 85748, Germany

    Paola Coan & Paul C. Diemoz

  4. European Synchrotron Radiation Facility, 38000, Grenoble, France

    Paola Coan & Paul C. Diemoz

  5. Department of Imaging Sciences, University of Rochester, Rochester, NY, 14627, USA

    Markus B. Huber & Axel Wismüller

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  1. Mahesh B. Nagarajan
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Correspondence to Mahesh B. Nagarajan.

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Nagarajan, M.B., Coan, P., Huber, M.B. et al. Computer-Aided Diagnosis for Phase-Contrast X-ray Computed Tomography: Quantitative Characterization of Human Patellar Cartilage with High-Dimensional Geometric Features. J Digit Imaging 27, 98–107 (2014). https://doi.org/10.1007/s10278-013-9634-3

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  • DOI: https://doi.org/10.1007/s10278-013-9634-3

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