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Deep Learning‐based Automatic Pipeline for Quantitative Assessment of Thigh Muscle Morphology and Fatty Infiltration by Sibaji Gaj; Brendan L. Eck; Dongxing Xie; Richard Lartey; Charlotte Lo; William Zaylor; Mingrui Yang; Kunio Nakamura; Carl S. Winalski; Kurt P. Spindler; Xiaojuan Li is a Medicine article available to read on EtoBox.

What is Deep Learning‐based Automatic Pipeline for Quantitative Assessment of Thigh Muscle Morphology and Fatty Infiltration about?

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 seg

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Author

Sibaji Gaj; Brendan L. Eck; Dongxing Xie; Richard Lartey; Charlotte Lo; William Zaylor; Mingrui Yang; Kunio Nakamura; Carl S. Winalski; Kurt P. Spindler; Xiaojuan Li

Publisher

Wiley

Published

2023

Language

EN

Field

Medicine (Health Sciences)