ArticleCartilage2021
Reproducibility of an Automated Quantitative MRI Assessment of Low-Grade Knee Articular Cartilage Lesions.
Article in Cartilage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Clinical follow-up 7-12 years after autologous chondrocyte implantation with a hydrogel scaffold (CaReS©) in knee cartilage defects.Wiener klinische Wochenschrift · 2026Article
- Magnetic resonance fingerprinting for the whole knee articular cartilage assessment using automated pipeline.European radiology · 2026Article
- The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning.Scientific reports · 2024Article
- 3D-DESS MRI with CAIPIRINHA two- and fourfold acceleration for quantitatively assessing knee cartilage morphology.Skeletal radiology · 2024Article
- A reproducibility study of knee cartilage volume and thickness values derived by fully automatic segmentation based on three-dimensional dual-echo in steady state data from 1.5 T and 3 T magnetic resonance imaging.Magma (New York, N.Y.) · 2024Article
- Quantitative MRI evaluation of articular cartilage in patients with meniscus tear.Frontiers in endocrinology · 2022Article
- Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning.Cartilage · 2021Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 4 countries.
Funding
Abstract
objectiveThe goal of this study was to assess the reproducibility of an automated knee cartilage segmentation of 21 cartilage regions with a model-based algorithm and to compare the results with manual segmentation.
designThirteen patients with low-grade femoral cartilage defects were included in the study and were scanned twice on a 7-T magnetic resonance imaging (MRI) scanner 8 days apart. A 3-dimensional double-echo steady-state (3D-DESS) sequence was used to acquire MR images for automated cartilage segmentation, and T2-mapping was performed using a 3D triple-echo steady-state (3D-TESS) sequence. Cartilage volume, thickness, and T2 and texture features were automatically extracted from each knee for each of the 21 subregions. DESS was used for manual cartilage segmentation and compared with automated segmentation using the Dice coefficient. The reproducibility of each variable was expressed using standard error of measurement (SEM) and smallest detectable change (SDC).
resultsThe Dice coefficient for the similarity between manual and automated segmentation ranged from 0.83 to 0.88 in different cartilage regions. Test-retest analysis of automated cartilage segmentation and automated quantitative parameter extraction revealed excellent reproducibility for volume measurement (mean SDC for all subregions of 85.6 mm
conclusionsThe proposed technique of automated knee cartilage evaluation based on the segmentation of 3D MR images and correlation with T2 mapping provides highly reproducible results and significantly reduces the segmentation effort required for the analysis of knee articular cartilage in longitudinal studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.