Authors:
Kana Matsuo
1
;
Koji Fujita
2
;
Takafumi Koyama
3
;
Shingo Morishita
3
and
Yuta Sugiura
1
Affiliations:
1
Department of Information and Computer Science, Keio University, Kanagawa, Japan
;
2
Department of Functional Joint Anatomy, Tokyo Medical and Dental University, Tokyo, Japan
;
3
Department of Orthopedic and Spinal Surgery, Tokyo Medical and Dental University, Tokyo, Japan
Keyword(s):
Measurement System, Deep Learning, Cervical Spine, Range of Motion.
Abstract:
Diseases of the cervical spine often cause more serious impediments to daily activities than diseases of other parts of the body, and thus require prompt and accurate diagnosis. One of the indicators used for diagnosing cervical spine diseases is measurements of the range of motion (RoM) angle. However, the main measurement method is manual, which creates a burden on physicians. In this work, we investigate the possibility of measuring the RoM angle of the cervical spine from cervical X-ray images by using Mask R-CNN and image processing. The results of measuring the RoM angle with the proposed cervical spine motion angle measurement system showed that the mean error from the true value was 3.5 degrees and the standard deviation was 2.8 degrees. Moreover, the standard deviation of the specialist measurements used for comparison was 2.9 degrees, while that of the proposed system was just 0 degrees, indicating that there was no variation in the measurements of the proposed system.