Symmetric Perception and Ordinal Regression for Detecting Scoliosis Natural Image

TL;DR

This paper introduces a novel dual-path neural network leveraging symmetry and ordinal relationships to detect scoliosis from natural back images, offering a non-invasive, cost-effective screening alternative that outperforms existing methods.

Contribution

The paper proposes a symmetric feature matching module and an ordinal regression head for scoliosis detection from natural images, addressing symmetry and severity level challenges.

Findings

Achieves 95.11% accuracy in general severity estimation.

Achieves 81.46% accuracy in fine-grained severity estimation.

Outperforms state-of-the-art methods and human experts.

Abstract

Scoliosis is one of the most common diseases in adolescents. Traditional screening methods for the scoliosis usually use radiographic examination, which requires certified experts with medical instruments and brings the radiation risk. Considering such requirement and inconvenience, we propose to use natural images of the human back for wide-range scoliosis screening, which is a challenging problem. In this paper, we notice that the human back has a certain degree of symmetry, and asymmetrical human backs are usually caused by spinal lesions. Besides, scoliosis severity levels have ordinal relationships. Taking inspiration from this, we propose a dual-path scoliosis detection network with two main modules: symmetric feature matching module (SFMM) and ordinal regression head (ORH). Specifically, we first adopt a backbone to extract features from both the input image and its horizontally flipped image. Then, we feed the two extracted features into the SFMM to capture symmetric relationships. Finally, we use the ORH to transform the ordinal regression problem into a series of binary classification sub-problems. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods as well as human performance, which provides a promising and economic solution to wide-range scoliosis screening. In particular, our method achieves accuracies of 95.11% and 81.46% in estimation of general severity level and fine-grained severity level of the scoliosis, respectively.