Magnetic resonance image segmentation using semi-automated software for quantification of knee articular cartilage—initial evaluation of a technique for paired scans

Abstract

Purpose-Software-based image analysis is important for studies of cartilage changes in knee osteoarthritis (OA). This study describes an evaluation of a semi-automated cartilage segmentation software tool capable of quantifying paired images for potential use in longitudinal studies of knee OA. We describe the methodology behind the analysis and demonstrate its use by determination of test-retest analysis precision of duplicate knee magnetic resonance imaging (MRI) data sets. © ISS 2009 jduryea@bwh.harvard.edu . Conflict of Interest Statement ES, RJ, JY, and CBE received direct salary support or had fee for service contracts associated with the OAI. In particular: ES is the principal of SciTrials, LLC, is the NIAMS OAI Technical Advisor and is under contract to NIAMS for this purpose; RJ and JU are at The Ohio State University that is under contract (N01-AR-2-2261) as a clinical center for the OAI; CBE is at the Memorial Hospital of Rhode Island that is under contract (N01-AR-2-2262) as a clinical center for the OAI. NIH Public Access Author ManuscriptSkeletal Radiol. Author manuscript; available in PMC 2011 January 10. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptMethods-Test-retest knee MR images of 12 subjects with a range of knee health were evaluated from the Osteoarthritis Initiative (OAI) pilot MR study. Each subject was removed from the magnet between the two scans. The 3D DESS (sagittal, 0.456 mm×0.365 mm, 0.7 mm slice thickness, TR 16.5 ms, TE 4.7 ms) images were obtained on a 3-T Siemens Trio MR system with a USA Instruments quadrature transmit-receive extremity coil. Segmentation of one 3D-image series was first performed and then the corresponding retest series was segmented by viewing both image series concurrently in two adjacent windows. After manual registration of the series, the first segmentation cartilage outline served as an initial estimate for the second segmentation. We evaluated morphometric measures of the bone and cartilage surface area (tAB and AC), cartilage volume (VC), and mean thickness (ThC.me) for medial/lateral tibia (MT/LT), total femur (F) and patella (P). Test-retest reproducibility was assessed using the root-mean square coefficient of variation (RMS CV%).Results-For the paired analyses, RMS CV % ranged from 0.9% to 1.2% for VC, from 0.3% to 0.7% for AC, from 0.6% to 2.7% for tAB and 0.8% to 1.5% for ThC.me. MRI data sets can be assessed visually using qualitative or semi-quantitative methods whereby a reader evaluates the image features according to a consistent set of rules or by comparison to representative cases from an atlas [3,[7][8][9]. MRI data sets are inherently digital, therefore, it is also possible to use software-based methods to evaluate the images and produce outcome measures for clinical studies. A common class of these software applications is the segmentation algorithm. The goal of segmentation software is to outline the relevant structure in space and identify which pixels or voxels belong to the object. For some ...

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