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Miss Jane's Building (Edisto Library Temporary Location)
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Volumetric quantitative characterization of human patellar cartilage with topological and geometrical features on phase-contrast X-ray computed tomography.
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- Author(s): Nagarajan, Mahesh1 ; Coan, Paola; Huber, Markus1; Diemoz, Paul; Wismüller, Axel; Nagarajan, Mahesh B2; Huber, Markus B2; Diemoz, Paul C3,4; Wismüller, Axel2,5
- Source:
Medical & Biological Engineering & Computing. Nov2015, Vol. 53 Issue 11, p1211-1220. 10p. 1 Color Photograph, 2 Charts, 4 Graphs.- Subject Terms:
*RESEARCH; *RESEARCH funding; OSTEOARTHRITIS; CARTILAGE cells; PHASE-contrast microscopy; COMPUTED tomography; DIAGNOSTIC imaging; MINKOWSKI geometry; SUPPORT vector machines; COMPARATIVE studies; KNEE diseases; RESEARCH methodology; MEDICAL cooperation; COMPUTERS in medicine; THREE-dimensional imaging; EVALUATION research - Source:
- Additional Information
- 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. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Medical & Biological Engineering & Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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