Quantification of cartilage loss in local regions of knee joints using semi-automated segmentation software: analysis of longitudinal data from the Osteoarthritis Initiative (OAI)

利用半自动分割软件量化膝关节局部区域的软骨损失:骨关节炎倡议(OAI)纵向数据分析

阅读:1

Abstract

INTRODUCTION: Quantitative cartilage morphometry is a valuable tool to assess osteoarthritis (OA) progression. Current methodologies generally evaluate cartilage morphometry in a full or partial sub-region of the cartilage plates. This report describes the evaluation of a semi-automated cartilage segmentation software tool capable of quantifying cartilage loss in a local indexed region. METHODS: We examined the baseline and 24-month follow-up MRI image sets of twenty-four subjects from the progression cohort of Osteoarthritis Initiative (OAI), using the Kellgren-Lawrence (KL) score of 3 at baseline as the inclusion criteria. A radiologist independently marked a single region of local thinning for each subject, and three additional readers, blinded to time point, segmented the cartilage using a semi-automated software method. Each baseline-24-month segmentation pair was then registered in 3D and the change in cartilage volume was measured. RESULTS: After 3D registration, the change in cartilage volume was calculated in specified regions centered at the marked point, and for the entire medial compartment of femur. The responsiveness was quantified using the standardized response mean (SRM) values and the percentage of subjects that showed a loss in cartilage volume. The most responsive measure of change was SRM=-1.21, and was found for a region of 10mm from the indexed point. DISCUSSION: The results suggest that measurement of cartilage loss in a local region is superior to larger areas and to the total plate. There also may be an optimal region size (10mm from an indexed point) in which to measure change. In principle, the method is substantially faster than segmenting entire plates or sub-regions.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。