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: 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.
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ABSTRACT: The pathogenesis of osteoarthritis (OA) includes complex events in the whole joint. Cartilage loss and bone remodelling are central in OA progression. In this project, we investigated the feasibility of quantifying OA by analysis of the tibial trabecular bone structure in low-field knee magnetic resonance imaging (MRI). The development of automatic and more sensitive indicators of OA in conjunction with low cost equipment have the potential to decrease the length and cost of clinical trials. We present a texture analysis methodology that combined uncommitted machine-learning techniques in a fully automatic framework. Different linear feature selection approaches where investigated. The methodology was evaluated in a longitudinal study, where MRI scans of knees were used to quantify the tibial trabecular bone in a bone marker for OA diagnosis and another marker for prediction of tibial cartilage loss. The healthy and diseased subjects were defined by the Kellgren and Lawrence index assigned by radiologists and the levels of cartilage loss were assessed by a segmentation process. A preliminary radiological reading of the knees with high and low risks of cartilage loss suggested the prognosis bone marker captured aspects of the vertical trabecularization of the tibial bone to define the prognosis of cartilage loss. We also investigated which region of the tibia provided the best prognosis for medial tibial cartilage loss. The structure of the tibial trabecular bone was divided in localized subregions in an attempt to capture the different pathological features occurring at each location. We applied multiple-instance learning, where each subregion was defined to be one instance and a bag held all instances over a full region-of-interest. The inferior part of the tibial bone was classified as the most relevant region for prognosis of cartilage loss and a preliminary radiological reading of a subset of the samples suggested the bone marker also captured the vertical trabecularization of the tibial bone to define the most relevant region. In a clinical point of view, besides presenting a bone marker able to predict disease progression and diagnostic bone marker superior to other OA biomarkers, our findings underlined the importance of the trabecular bone to the understanding of the OA pathology.04/2013, Degree: PhD, Supervisor: Mads Nielsen and Erik B. Dam
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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; DOI:10.1117/12.844115 · 0.20 Impact Factor