TU Darmstadt / ULB / TUbiblio

Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative

Tack, A. ; Mukhopadhyay, A. ; Zachow, S. (2018)
Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative.
In: Osteoarthritis and Cartilage, 26 (5)
doi: 10.1016/j.joca.2018.02.907
Artikel, Bibliographie

Kurzbeschreibung (Abstract)

Objective: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof. Method: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MRI Osteoarthritis Knee Score (MOAKS) experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression. Results: Segmentation accuracy measured as dice similarity coefficient was 83.8% for medial menisci (MM) and 88.9% for lateral menisci (LM) at baseline, and 83.1% and 88.3% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (r ¼ 0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16 ± 0.93 mm vs 0.83 ± 0.92 mm; P < 0.05). Conclusion: Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA.

Typ des Eintrags: Artikel
Erschienen: 2018
Autor(en): Tack, A. ; Mukhopadhyay, A. ; Zachow, S.
Art des Eintrags: Bibliographie
Titel: Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative
Sprache: Englisch
Publikationsjahr: 2018
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Osteoarthritis and Cartilage
Jahrgang/Volume einer Zeitschrift: 26
(Heft-)Nummer: 5
DOI: 10.1016/j.joca.2018.02.907
URL / URN: https://doi.org/10.1016/j.joca.2018.02.907
Kurzbeschreibung (Abstract):

Objective: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof. Method: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MRI Osteoarthritis Knee Score (MOAKS) experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression. Results: Segmentation accuracy measured as dice similarity coefficient was 83.8% for medial menisci (MM) and 88.9% for lateral menisci (LM) at baseline, and 83.1% and 88.3% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (r ¼ 0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16 ± 0.93 mm vs 0.83 ± 0.92 mm; P < 0.05). Conclusion: Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA.

Freie Schlagworte: Magnetic resonance imaging (MRI), Statistical shape models (SSM), Deep learning
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
Hinterlegungsdatum: 01 Jul 2019 08:38
Letzte Änderung: 01 Jul 2019 08:38
PPN:
Export:
Suche nach Titel in: TUfind oder in Google
Frage zum Eintrag Frage zum Eintrag

Optionen (nur für Redakteure)
Redaktionelle Details anzeigen Redaktionelle Details anzeigen