Automated Recognition of Erector Spinae Muscles and Their Skeletal Attachment Region via Deep Learning in Torso CT Images
Erector spinae muscle (ESM) is an important muscle in the torso region. Changes of sizes, shapes and densities in the cross section of the spinal column muscles have been found in chronic low back pain, degenerative lumbar sclerosis and chronic obstructive pulmonary disease. However, the image features of the ESM are measured manually by the physician. Therefore, automatic recognition in three dimensions (3D) not only for the limited two-dimensional (2D) section but also for the whole ESM is required. In this study, we realize automatic recognition of the ESMs and its attachment region on the skeleton using a 2D deep convolutional neural network. Each cross section of the 3D computed tomography (CT) image is input as a 2D image to the fully convolutional network. Then, the obtained result is reconstructed into a 3D image to obtain the recognition result of the ESM and its attachment region on the skeleton. ESM and attached area are extracted manually from the CT images of 11 cases and used for evaluation. In the experiments, automatic recognition was performed for each case using the leave-one-out method. The mean recognition accuracy of ESM and attached area was \(89.9\%\) and \(65.5\%\), respectively for the Dice coefficient. In this study, although there is over-extraction in the recognition of the attachment region, the initial region has been acquired successfully and it is the first study to simultaneously recognize the ESMs and its attachment region on the skeleton.
KeywordsErector spinae muscles Skeletal muscles Deep convolutional neural networks Fully convolutional networks
This research was supported in part by a Grant-in-Aid for Scientific Research on Innovative Areas (Grant No. 26108005 and 17H05301), MEXT, Japan.
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