Automatic quantification of local and global articular cartilage surface curvature: Biomarkers for osteoarthritis?

Folkesson, Jenny; Dam, Erik B.; Olsen, Ole; Karsdal, Morten A.; Pettersen, P.C.; Christiansen, Claus

doi:10.1002/mrm.21560

Cited by 20 publications

(12 citation statements)

References 24 publications

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“…Curvature estimates at different scales (at 0.2 T) were reported to be associated with the magnitude of cartilage loss longitudinally [40] and cartilage homogeneity (quantified by measuring entropy from the distribution of signal intensities in tibial cartilage from 0.2 T gradient echo images) was reported to discriminate between subjects without and with early radiographic OA [18]. This measure was proposed to be particularly sensitive in peripheral regions, where the cartilage is covered by the meniscus [41].…”

Section: Cartilage Morphology Outcome Variablesand Regions Of Intementioning

confidence: 99%

“…The use of peripheral MRI scanners at lower field strengths potentially permits more widespread distribution of this technology, especially when access to high-field MRI is limited. Quantitative cartilage measurement at 0.2 T have also been proposed [18, 27, 28, 38–40] but have not been validated versus external standards or measurement at higher field strength. However, they were shown to display substantially larger precision errors than measurements performed at higher field strength.…”

Section: Imaging Protocols For Measurement Ofcartilage Morphology mentioning

confidence: 99%

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Quantitative Cartilage Imaging in Knee Osteoarthritis

Eckstein

Wirth

2011

Arthritis

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Quantitative measures of cartilage morphology (i.e., thickness) represent potentially powerful surrogate endpoints in osteoarthritis (OA). These can be used to identify risk factors of structural disease progression and can facilitate the clinical efficacy testing of structure modifying drugs in OA. This paper focuses on quantitative imaging of articular cartilage morphology in the knee, and will specifically deal with different cartilage morphology outcome variables and regions of interest, the relative performance and relationship between cartilage morphology measures, reference values for MRI-based knee cartilage morphometry, imaging protocols for measurement of cartilage morphology (including those used in the Osteoarthritis Initiative), sensitivity to change observed in knee OA, spatial patterns of cartilage loss as derived by subregional analysis, comparison of MRI changes with radiographic changes, risk factors of MRI-based cartilage loss in knee OA, the correlation of MRI-based cartilage loss with clinical outcomes, treatment response in knee OA, and future directions of the field.

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Section: Cartilage Morphology Outcome Variablesand Regions Of Intementioning

confidence: 99%

Section: Imaging Protocols For Measurement Ofcartilage Morphology mentioning

confidence: 99%

Quantitative Cartilage Imaging in Knee Osteoarthritis

Eckstein

Wirth

2011

Arthritis

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“…This reader bottleneck makes comprehensive analysis infeasible-if based on manual segmentation. Furthermore, more advanced three-dimensional (3-D) morphometric markers, such as cartilage surface smoothness 5 or joint congruity, 6 will suffer from the inter-slice discontinuity artifacts caused by slice-wise delineation. Finally, inclusion of additional structures beyond cartilage only increases the quantification challenge.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative

Dam¹,

Lillholm²,

Marques³

et al. 2015

J. Med. Imag

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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.

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“…In this study, we were limited to only texture markers. However, validating other markers related to disc surface quality and biomechanics between disc and vertebral body similar to studies described in 20,21 is planned in future studies.…”

Section: New and Discussionmentioning

confidence: 93%

Prognosis of intervertebral disc loss from diagnosis of degenerative disc disease

Li¹,

Lin²,

Tay³

et al. 2015

SPIE Proceedings

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Degenerative Disc Diseas (DDD) is one of the most common causes of low back pain, and is a major factor in limiting the quality of life of an individual usually as they enter older stages of life, the disc degeneration reduces the shock absorption available which in turn causes pain. Disc loss is one of the central processes in the pathogenesis of DDD. In this study, we investigated whether the image texture features quantified from magnetic resonance imaging (MRI) could be appropriate markers for diagnosis of DDD and prognosis of inter-vertebral disc loss. The main objective is to use simple image based biomarkers to perform prognosis of spinal diseases using non-invasive procedures. Our results from 65 subjects proved the higher success rates of the combination marker compared to the individual markers and in the future, we will extend the study to other spine regions to allow prognosis and diagnosis of DDD for a wider region.

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Automatic quantification of local and global articular cartilage surface curvature: Biomarkers for osteoarthritis?

Cited by 20 publications

References 24 publications

Quantitative Cartilage Imaging in Knee Osteoarthritis

Quantitative Cartilage Imaging in Knee Osteoarthritis

Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative

Prognosis of intervertebral disc loss from diagnosis of degenerative disc disease

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