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

Abstract

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.

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

“…Since 2021, there has been an exponential increase in studies on custom architecture CNNs for the diagnosis of ACL injuries applied to MRI, and currently there are various DL models developed, such as VGG16, VGG19, U-Net, AdaBoost, XGBoost, Xception, MRPyrNet, Inception ResNet-v2, RadImageNet, and Inception-v3 DTL [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51]. Awan et al introduced a method that utilizes a tailored 14-layer ResNet-14 configuration of a CNN, which processes data in six distinct directions.…”