Segmentation of hip cartilage in compositional magnetic resonance imaging: A fast, accurate, reproducible, and clinically viable semi-automated methodology

Journal of Orthopaedic Research : Official Publication of the Orthopaedic Research Society
Scott FernquestSion Glyn-Jones

Abstract

Manual segmentation is a significant obstacle in the analysis of compositional MRI for clinical decision-making and research. Our aim was to produce a fast, accurate, reproducible, and clinically viable semi-automated method for segmentation of hip MRI. We produced a semi-automated segmentation method for cartilage segmentation of hip MRI sequences consisting of a two step process: (i) fully automated hierarchical partitioning of the data volume generated using a bespoke segmentation approach applied recursively, followed by (ii) user selection of the regions of interest using a region editor. This was applied to dGEMRIC scans at 3T taken from a prospective longitudinal study of individuals considered at high-risk of developing osteoarthritis (SibKids) which were also manually segmented for comparison. Fourteen hips were segmented both manually and using our semi-automated method. Per hip, processing time for semi-automated and manual segmentation was 10-15, and 60-120 min, respectively. Accuracy and Dice similarity coefficient (DSC) for the comparison of semi-automated and manual segmentations was 0.9886 and 0.8803, respectively. Intra-observer and inter-observer reproducibility of the semi-automated segmentation method gave a...Continue Reading

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Citations

Oct 24, 2019·Current Opinion in Rheumatology·Vladimir JurasRavinder R Regatte
May 13, 2020·NMR in Biomedicine·Ekaterina BruiAnna Andreychenko
Jul 18, 2020·International Journal of Computer Assisted Radiology and Surgery·Hari McGrathTom Vercauteren
Apr 19, 2019·Clinical Orthopaedics and Related Research·Florian SchmaranzerGuoyan Zheng
Feb 24, 2020·Computer Methods and Programs in Biomedicine·Amélie PoilliotNiels Hammer
Apr 30, 2021·Modern Rheumatology·Veronica K ChengAkihiro Sudo

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