Automated segmentation of 2D low-dose CT images of the psoas-major muscle using deep convolutional neural networks
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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.
KeywordsPsoas-major muscle Deep learning Convolutional neural networks X-ray computed tomography Automated segmentation Sarcopenia
We would like to thank the staff of the Hamamatsu Medical Imaging Center and Hamamatsu Photonics K. K. for their support.
Compliance with ethical standards
Conflict of interest
We declare that this work is free from financial limitation or any other relationship that might lead to a conflict of interest. The authors have no conflicts of interest to declare.
Statement of human and animal rights
All procedures performed in the studies involving human participants were in accordance with the ethical standards of the Institutional Review Board of Hamamatsu Medical Photonics Foundation and 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies performed with animals.
Informed consent was obtained from all study participants.
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