Estimation of patient’s angle from skull radiographs using deep learning

Article type: Research Article

Authors: Nakazeko, Kazuma^{a; b; *} | Kojima, Shinya^c | Watanabe, Hiroyuki^d | Kudo, Hiroyuki^e

Affiliations: [a] Department of Radiological Technology, Faculty of Health Science, Juntendo University, Yushima, Bunkyo-Ku, Tokyo, Japan | [b] Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan | [c] Department of Medical Radiology, Faculty of Medical Technology, Teikyo University, Kaga, Itabashi-Ku, Tokyo, Japan | [d] Graduate School of Health Sciences, Showa University, Tookaichibacho, Midori-ku, Yokohama, Kanagawa, Japan | [e] Graduate School of Systems and Information Engineering, University of Tsukuba, Tennodai, Tsukuba, Ibaraki, Japan

Correspondence: [*] Corresponding author: Kazuma Nakazeko, Department of Radiological Technology, Faculty of Health Science, Juntendo University, 1-5-32, Yushima, Bunkyo-Ku, Tokyo, Japan. Tel.: +81 03 3812 1780 /Ext: 3965; Fax: +81 03 3812 1781; E-mail: k.nakazeko.tk@juntendo.ac.jp.

Abstract: BACKGROUND:Skull radiography, an assessment method for initial diagnosis and post-operative follow-up, requires substantial retaking of various types of radiographs. During retaking, a radiologic technologist estimates a patient’s rotation angle from the radiograph by comprehending the relationship between the radiograph and the patient’s angle for adequate assessment, which requires extensive experience. OBJECTIVE:To develop and test a new deep learning model or method to automatically estimate patient’s angle from radiographs. METHODS:The patient’s position is assessed using deep learning to estimate their angle from skull radiographs. Skull radiographs are simulated using two-dimensional projections from head computed tomography images and used as input data to estimate the patient’s angle, using deep learning under supervised training. A residual neural network model is used where the rectified linear unit is changed to a parametric rectified linear unit, and dropout is added. The patient’s angle is estimated in the lateral and superior-inferior directions. RESULTS:Applying this new deep learning model, the estimation errors are 0.56±0.36° and 0.72±0.52° in the lateral and superior-inferior angles, respectively. CONCLUSIONS:These findings suggest that a patient’s angle can be accurately estimated from a radiograph using a deep learning model leading to reduce retaking time, and then used to facilitate skull radiography.

Keywords: Skull radiography, radiographs, patient’s angle, retaking, deep learning, ResNet

DOI: 10.3233/XST-221200

Journal: Journal of X-Ray Science and Technology, vol. 30, no. 5, pp. 1033-1045, 2022