4.5 Article

Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration

期刊

MAGNETIC RESONANCE IN MEDICINE
卷 89, 期 6, 页码 2441-2455

出版社

WILEY
DOI: 10.1002/mrm.29599

关键词

automated segmentation; deep learning; magnetic resonance imaging; thigh muscle

向作者/读者索取更多资源

Fast and accurate segmentation of thigh muscles from MRI is crucial for quantifying thigh muscle morphology and composition. A new deep learning-based segmentation model was developed for automated and reproducible cross-sectional area (CSA) and fat fraction (FF) quantification. The model was tested on patients who underwent anterior cruciate ligament reconstructions 10 years ago.
Purpose Fast and accurate thigh muscle segmentation from MRI is important for quantitative assessment of thigh muscle morphology and composition. A novel deep learning (DL) based thigh muscle and surrounding tissues segmentation model was developed for fully automatic and reproducible cross-sectional area (CSA) and fat fraction (FF) quantification and tested in patients at 10 years after anterior cruciate ligament reconstructions.Methods A DL model combining UNet and DenseNet was trained and tested using manually segmented thighs from 16 patients (32 legs). Segmentation accuracy was evaluated using Dice similarity coefficients (DSC) and average symmetric surface distance (ASSD). A UNet model was trained for comparison. These segmentations were used to obtain CSA and FF quantification. Reproducibility of CSA and FF quantification was tested with scan and rescan of six healthy subjects.Results The proposed UNet and DenseNet had high agreement with manual segmentation (DSC > 0.97, ASSD < 0.24) and improved performance compared with UNet. For hamstrings of the operated knee, the automated pipeline had largest absolute difference of 6.01% for CSA and 0.47% for FF as compared to manual segmentation. In reproducibility analysis, the average difference (absolute) in CSA quantification between scan and rescan was better for the automatic method as compared with manual segmentation (2.27% vs. 3.34%), whereas the average difference (absolute) in FF quantification were similar.Conclusions The proposed method exhibits excellent accuracy and reproducibility in CSA and FF quantification compared with manual segmentation and can be used in large-scale patient studies.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据