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Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative (Supplementary Material)

  • 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 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 (ρ=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.
Author:Alexander TackORCiD, Anirban Mukhopadhyay, Stefan ZachowORCiD
Document Type:Research data
Resource Type General:Software
Year of first publication:2018
Supplementary data to reproduce results from the related publication, including convolutional neural networks' weights.
Date data created:2018
Geolocation:Berlin, Germany
Related Identifier:https://doi.org/10.12752/4.TMZ.1.0
Software description :The convolutional neural networks' weights were generated using Keras with the backend Theano. The networks were trained on segmentation masks of medial and lateral menisci provided by iMorphics (Manchester, UK). See https://github.com/AlexanderTack/Menisci-Segmentation for the python source code.
Download Url:http://www.zib.de/ext-data/2018_Tack_OAC-Supplementary-Material.zip
Licence (German):License LogoCreative Commons - Namensnennung
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