Automatic Landmark Detection of Human Back Surface from Depth Images via Deep Learning

Abstract

Studying human postural structure is one of the challenging issues among scholars and physicians. The spine is known as the central axis of the body, and due to various genetic and environmental reasons, it could suffer from deformities leading to physical dysfunction and correspondingly affecting people's quality of life. Radiography is the most common method for detecting these deformities; however, it frequently exposes the patient to X-rays and ionization and consequently increases cancer risk in patients particularly children and pregnant women.The purpose of this research is to provide an entirely safe and non-invasive method to examine the spiral structure and its deformities. Hence, it is attempted to find the exact location of anatomical landmarks on the human back surface, which provides useful and functional information about the status of the human postural structure to the physician. In this study, using Microsoft Kinect sensor, the depth images from the human back surface of 105 people were recorded and, our proposed approach – a deep convolution neural network- was used as a model to estimate the locations of anatomical landmarks. In network architecture, two learning processes, including landmark position and affinity between the two associated landmarks, are successively performed in two separate branches. This is a bottom-up approach; thus, the runtime complexity is considerably reduced, and then the resulting anatomical points are evaluated concerning manual landmarks marked by the operator as the benchmark. Our results showed 86.9% of PDJ and 80% of PCK that demonstrate more effectiveness compared to other methods.