Automatic quantification of local and global articular cartilage surface curvature: biomarkers for osteoarthritis?
ABSTRACT The objective of this study was to quantitatively assess the surface curvature of the articular cartilage from low-field magnetic resonance imaging (MRI) data, and to investigate its role in populations with varying radiographic signs of osteoarthritis (OA), cross-sectionally and longitudinally. The curvature of the articular surface of the medial tibial compartment was estimated both on fine and coarse scales using two different automatic methods which are both developed from an automatic 3D segmentation algorithm. Cross-sectionally (n=288), the surface curvature for both the fine- and coarse-scale estimates were significantly higher in the OA population compared with the healthy population, with P<0.001 and P<0.001, respectively. For the longitudinal study (n=245), there was a significant increase in fine-scale curvature for healthy and borderline OA populations (P<0.001), and in coarse-scale curvature for severe OA populations (P<0.05). Fine-scale curvature could predict progressors using the estimates of those healthy at baseline (P<0.001). The inter-scan precision was 2.2 and 6.5 (mean CV) for the fine- and coarse scale curvature measures, respectively. The results showed that quantitative curvature estimates from low-field MRI at different scales could potentially become biomarkers targeted at different stages of OA.
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ABSTRACT: Fully automatic imaging biomarkers may allow quantification of patho-physiological processes that a radiologist would not be able to assess reliably. This can introduce new insight but is problematic to validate due to lack of meaningful ground truth expert measurements. Rather than quantification accuracy, such novel markers must therefore be validated against clinically meaningful end-goals such as the ability to allow correct diagnosis. We present a method for automatic cartilage surface smoothness quantification in the knee joint. The quantification is based on a curvature flow method used on tibial and femoral cartilage compartments resulting from an automatic segmentation scheme. These smoothness estimates are validated for their ability to diagnose osteoarthritis and compared to smoothness estimates based on manual expert segmentations and to conventional cartilage volume quantification. We demonstrate that the fully automatic markers eliminate the time required for radiologist annotations, and in addition provide a diagnostic marker superior to the evaluated semi-manual markers.Proceedings of SPIE - The International Society for Optical Engineering 03/2010; · 0.20 Impact Factor
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ABSTRACT: In this paper, we present an evaluation framework for the 3D segmentation of knee bones and cartilage from magnetic resonance im-ages. The framework was established for one of the three challenges at the "Medical Image Analysis for the Clinic: A Grand Challenge" workshop held at the 2010 Medical Image Computing and Computer Assisted Inter-vention (MICCAI) conference in Beijing, China. After this workshop, the framework will remain open to online submissions via www.ski10.org. We describe the motivation for this challenge, the preparation of training and test datasets, and the evaluation measures used to rate submitted results.