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- W4324141926 abstract "Purpose: Automated, U-Net-based cartilage segmentations have been reported to provide a high sensitivity to cartilage thickness loss. However, a persistent concern regarding automated segmentations is the imperfect agreement between automated and manually performed segmentations, particularly in knees with established radiographic osteoarthritis (OA). Incorrect automated segmentations may impair the sensitivity to change or to differences in change between groups. Currently, expert-based manual segmentation is therefore still considered to be the “gold standard”. The purpose of this study was to evaluate the performance of automated, U-Net-based cartilage segmentations of MRIs with and without additional manual quality control and correction (QC&C) and the time effort required for QC&C. To this end, automated segmentation with and without QC&C was compared with the manual “gold standard” cross-sectionally as well as longitudinally, with change measured over 24 months." @default.
- W4324141926 created "2023-03-15" @default.
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- W4324141926 date "2023-03-01" @default.
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- W4324141926 title "Do Automated, U-Net-Based Cartilage Segmentations From Mri Benefit From Manual Quality Control And Correction? Data From The Imi-Approach Project" @default.
- W4324141926 doi "https://doi.org/10.1016/j.joca.2023.01.286" @default.
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