Abstract
BackgroundWe have developed a fully automatic three-dimensional MRI analysis software program for automatic segmentation of knee cartilage using a deep neural network. The purpose of this study was to use this software to clarify the interscan measurement error of the knee cartilage thickness and projected cartilage area ratio at 9 regions and 45 subregions in the knee. MethodsTen healthy volunteers underwent MRI twice in the same day. The software provided cartilage thickness and projected cartilage area ratio (thickness ≥ 1.5 mm) at 9 regions and 45 subregions of the knee without any manual correction. The interscan measurement error was calculated at each region and subregion from the data of nine donors, except for one donor who had body motion during the MRI examination. ResultsThe interscan measurement error of cartilage thickness was less than 0.10 mm at all 9 regions and at 39 subregions among 45 subregions. The measurement errors ranged from 0.03 to 0.21 mm. The intraclass correlation coefficients (ICC) of cartilage thickness were higher than 0.75 at all 9 regions and 41 subregions. The interscan measurement error of the projected cartilage area ratio ranged from 0.01 to 0.03 for all 9 regions. ConclusionsThis study clarified the interscan measurement error of the knee cartilage thickness and projected cartilage area ratio.
Highlights
Three-dimensional magnetic resonance imaging (3D MRI) is an attractive analytical method for the quantification of cartilage [1]. This promising procedure is not popular at present because segmentation of cartilage often requires manual operation or correction that involves expenditure of considerable time and effort. We have addressed this problem by the recent development of software for automatic segmentation of cartilage using deep neural networks [2]
The accuracy of this software for knee MRI was comparable to that re ported previously by Liu et al [3] and Norman et al [4] in Dice simi larity coefficient (DSC) [5] between manual segmentation and automatic segmentation
The recent work of Hyodo et al proposed an alternative cartilage measurement, the “pro jected cartilage area ratio,” which is the ratio of the cartilage with intended thickness to the total area of the region of interest (ROI) [6]
Summary
Three-dimensional magnetic resonance imaging (3D MRI) is an attractive analytical method for the quantification of cartilage [1] This promising procedure is not popular at present because segmentation of cartilage often requires manual operation or correction that involves expenditure of considerable time and effort. We have addressed this problem by the recent development of software for automatic segmentation of cartilage using deep neural networks [2]. The purpose of this study was to use this software to clarify the interscan measurement error of the knee cartilage thickness and projected cartilage area ratio at 9 regions and 45 subregions in the knee. Conclusions: This study clarified the interscan measurement error of the knee cartilage thickness and projected cartilage area ratio
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