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Abstract-We present a fully automatic method for articular cartilage segmentation from magnetic resonance imaging (MRI) which we use as the foundation of a quantitative cartilage assessment. We evaluate our method by comparisons to manual segmentations by a radiologist and by examining the interscan reproducibility of the volume and area estimates. Training and evaluation of the method is performed on a data set consisting of 139 scans of knees with a status ranging from healthy to severely osteoarthritic. This is, to our knowledge, the only fully automatic cartilage segmentation method that has good agreement with manual segmentations, an interscan reproducibility as good as that of a human expert, and enables the separation between healthy and osteoarthritic populations. While high-field scanners offer high-quality imaging from which the articular cartilage have been evaluated extensively using manual and automated image analysis techniques, low-field scanners on the other hand produce lower quality images but to a fraction of the cost of their high-field counterpart. For low-field MRI, there is no well-established accuracy validation for quantitative cartilage estimates, but we show that differences between healthy and osteoarthritic populations are statistically significant using our cartilage volume and surface area estimates, which suggests that low-field MRI analysis can become a useful, affordable tool in clinical studies.

Objective: Osteoarthritis (OA) is the most common form of arthritic disease, and it is a major cause of disability and impaired quality of life in the elderly. OA is a complex disease of the entire joint, including bone and cartilage, thereby presenting alternative approaches for treatment. This review summarizes emerging observations from cell biology to preliminary clinical trials, describing interactions between the bone and cartilage components. We speculate whether a treatment for OA would be possible without targeting the bone compartment? Methods: Peer-reviewed articles found using pre-defined search criteria and published in the PubMed database until June 2007 are summarized. In addition, abstracts from the OsteoArthritis Research Society International (OARSI) conferences in the time period 2000e2007 were included.Results: Bone and cartilage health seem to be tightly associated. Ample evidence is found for bone changes during progression of OA, including, but not limited to, increased turnover in the subchondral bone, thinning of the trabecular structure, osteophytes, bone marrow lesions and sclerosis of the subchondral plate. In addition, a range of investigations has described secondary positive effects on cartilage health when bone resorption was suppressed, or deterioration of the cartilage when resorption is increased. Conclusion:An optimal treatment for OA might include targeting both the bone and cartilage compartments. Hence, as several cell systems are to be targeted in a safe manner, limited options seem possible.

The aim of this review is to discuss the potential usefulness of a novel class of biochemical markers, neoepitopes, in the context of the US Food and Drug Administration (FDA) Critical Path Initiative, which emphasizes biomarkers of safety and efficacy as areas of pivotal interest. Examples of protein degradation fragments--neoepitopes--that have proven useful for research on bone and cartilage are collagen type I and collagen type II degradation products, respectively. These markers have utility in the translational approach, as they can be used to estimate safety and efficacy in both preclinical models and clinical settings. Biochemical markers of tissue degradation may provide optimal tools, which in combination with other techniques, prove essential to drug discovery and development.

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Abstract. Clinical studies including thousands of magnetic resonance imaging (MRI) scans offer potential for pathogenesis research in osteoarthritis. However, comprehensive quantification of all bone, cartilage, and meniscus compartments is challenging. We propose a segmentation framework for fully automatic segmentation of knee MRI. The framework combines multiatlas rigid registration with voxel classification and was trained on manual segmentations with varying configurations of bones, cartilages, and menisci. The validation included high-and low-field knee MRI cohorts from the Center for Clinical and Basic Research, the osteoarthritis initiative (QAI), and the segmentation of knee images10 (SKI10) challenge. In total, 1907 knee MRIs were segmented during the evaluation. No segmentations were excluded. Our resulting OAI cartilage volume scores are available upon request. The precision and accuracy performances matched manual reader re-segmentation well. The cartilage volume scan-rescan precision was 4.9% (RMS CV). The Dice volume overlaps in the medial/lateral tibial/femoral cartilage compartments were 0.80 to 0.87. The correlations with volumes from independent methods were between 0.90 and 0.96 on the OAI scans. Thus, the framework demonstrated precision and accuracy comparable to manual segmentations. Finally, our method placed second for cartilage segmentation in the SKI10 challenge. The comprehensive validation suggested that automatic segmentation is appropriate for cohorts with thousands of scans.

Osteoarthritis (OA) is a disease of the entire joint. Different treatment strategies for OA have been proposed and tested clinically without the desired efficacy. One reason for the scarcity of current chondroprotective agents may be the insufficient understanding of the patho-physiology of the joint and whether the joint damage is reversible or irreversible. In this review, we compile emerging data on cellular and pathological aspects of OA, and ask whether these data could give clue to when cartilage degradation is reversible and whether a point-of-no-return exists. We highlight different stages of OA, and speculate whether different intervention strategies (e.g. DMOAD vs. SMOADs) may only be efficacious at distinct stages of OA.