Volumetric quantitative characterization of human patellar cartilage with topological and geometrical features on phase-contrast X-ray computed tomography.
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 demonst...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 53; no. 11; pp. 1211 - 1221 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Nov2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110654125&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110654125 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2015 vid: 53 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 110654125 110654125 NLM26142112 110654125 10.1007/s11517-015-1340-5 NLM26142112 PMC4630098 [Available on 11/01/16] 110654125 ppf: 1211 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Volumetric quantitative characterization of human patellar cartilage with topological and geometrical features on phase-contrast X-ray computed tomography. aug: au: Nagarajan, Mahesh Coan, Paola Huber, Markus Diemoz, Paul Wismüller, Axel Nagarajan, Mahesh B Huber, Markus B Diemoz, Paul C Wismüller, Axel affil: Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, Rochester USA sug: subj: Radiographic Image Interpretation, Computer-Assisted Methods Tomography, X-Ray Computed Methods Imaging, Three-Dimensional Methods Osteoarthritis, Knee Radiography Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Funding Source ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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