Detecting Spinal Alignment Through Depth Image

Abstract

Adolescent Idiopathic Scoliosis (AIS) is a common form of scoliosis found among adolescents. The abnormal spinal curvature along with twisting of the vertebrae requires early detection before the vertebrae alignment worsens. Furthermore, surgical treatment is crucial for those with severe conditions. In Japan, annual scoliosis screening is conducted during school health checkups. These screening procedures often involve an initial screening without the use of ionizing radiation (such as X-rays) and an X-ray screening later for those suspected with scoliosis. Currently, X-ray examinations produce the most accurate results for detecting the spinal position. However, noninvasive alternative methods, such as Moire imaging, provide satisfactory initial results. Unfortunately, the production of Moire cameras have halted in recent years. This paper proposes an alternative, non-invasive and non-ionizing radioactive method to detect spinal alignment. Depth images provide an extra dimension of information compared to RGB images. The proposed method utilizes this extra dimension of depth values to create different types of images that are then trained using Convolutional Neural Networks to predict the spinal alignment. Results indicate that Moire images reproduced from depth images produce the best spinal alignment. However, other types of images derived from the depth image also have higher accuracy when compared with the depth image itself.