EUFormer: Learning Driven 3D Spine Deformity Assessment with Orthogonal Optical Images

TL;DR

This paper introduces EUFormer, a novel deep learning model that accurately reconstructs 3D spine curves from orthogonal RGB images to improve scoliosis severity assessment without radiation exposure.

Contribution

The paper presents EUFormer, an efficient U-shape transformer architecture that enhances 3D spine curve reconstruction from 2D images for AIS grading, outperforming classical models.

Findings

EUFormer outperforms classical U-shape models in spine curve generation.

3D spine curve-based grading is more accurate than 2D-based methods.

The pipeline reduces reliance on radiographic examinations.

Abstract

In clinical settings, the screening, diagnosis, and monitoring of adolescent idiopathic scoliosis (AIS) typically involve physical or radiographic examinations. However, physical examinations are subjective, while radiographic examinations expose patients to harmful radiation. Consequently, we propose a pipeline that can accurately determine scoliosis severity. This pipeline utilizes posteroanterior (PA) and lateral (LAT) RGB images as input to generate spine curve maps, which are then used to reconstruct the three-dimensional (3D) spine curve for AIS severity grading. To generate the 2D spine curves accurately and efficiently, we further propose an Efficient U-shape transFormer (EUFormer) as the generator. It can efficiently utilize the learned feature across channels, therefore producing consecutive spine curves from both PA and LAT views. Experimental results demonstrate superior performance of EUFormer on spine curve generation against other classical U-shape models. This finding demonstrates that the proposed method for grading the severity of AIS, based on a 3D spine curve, is more accurate when compared to using a 2D spine curve.