Automated segmentation of 2D low-dose CT images of the psoas-major muscle using deep convolutional neural networks

Abstract

The psoas-major muscle has been reported as a predictive factor of sarcopenia. The cross-sectional area (CSA) of the psoas-major muscle in axial images has been indicated to correlate well with the whole-body skeletal muscle mass. In this study, we evaluated the segmentation accuracy of low-dose X-ray computed tomography (CT) images of the psoas-major muscle using the U-Net convolutional neural network, which is a deep-learning technique. Deep learning has been recently known to outperform conventional image-segmentation techniques. We used fivefold cross validation to validate the segmentation performance (n = 100) of the psoas-major muscle. For the intersection over union and CSA ratio, segmentation accuracies of 86.0 and 103.1%, respectively, were achieved. These results suggest that the U-Net network is competitive compared with the previous methods. Therefore, the proposed technique is useful for segmenting the psoas-major muscle even in low-dose CT images.

Keywords: Automated segmentation; Convolutional neural networks; Deep learning; Psoas-major muscle; Sarcopenia; X-ray computed tomography.