Deep Learning-based Segmentation of Knee MRI for Fully Automatic Sub-Regional Morphological Assessment of Cartilage Tissues: Data from the Osteoarthritis Initiative.

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Abstract

Morphological changes in knee cartilage sub-regions are valuable imaging-based biomarkers for understanding progression of osteoarthritis, and they are typically detected from MRI. So far, accurate segmentation of cartilage has been done manually. Deep learning approaches show high promise in automating the task; however, they lack clinically relevant evaluation. We introduce a fully automatic method for segmentation and sub-regional assessment of articular cartilage, and evaluate its predictive power in context of radiographic osteoarthritis progression. Two datasets of 3D DESS MRI derived from the Osteoarthritis Initiative were used: first (n=88), second (n=600, 0-/12-/24-month visits). Our method performed DL-based segmentation of knee cartilage tissues, their sub-regional division via multi-atlas-registration, and extraction of sub-regional volume and thickness. The segmentation model was developed and assessed on the first dataset. Subsequently, on the second dataset, the morphological measurements from our and the prior methods were analyzed in correlation and agreement, and, eventually, by their discriminative power of radiographic osteoarthritis progression over 12 and 24 months, retrospectively. The segmentation model showed very high correlation (r>0.934) and agreement (mean difference<116mm3 ) in volumetric measurements with the reference segmentations. Comparison of our and manual segmentation methods yielded r=0.845-0.973 and mean differences=262-501mm3 for weight-bearing cartilage volume, and r=0.770-0.962 and mean differences=0.513-1.138mm for sub-regional cartilage thickness. With regard to osteoarthritis progression, our method found most of the significant associations identified using the manual segmentation method, for both 12- and 24-month sub-regional cartilage changes. The method may be effectively applied in osteoarthritis progression studies to extract cartilage-related imaging biomarkers. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.