MMA-Net: Multiple Morphology-Aware Network for Automated Cobb Angle Measurement

TL;DR

MMA-Net is a novel deep learning framework that enhances automated Cobb angle measurement by integrating multiple spine morphological features, leading to improved accuracy and reliability in scoliosis assessment.

Contribution

The paper introduces MMA-Net, which combines multiple morphological cues from spine X-rays with attention mechanisms for more accurate Cobb angle measurement.

Findings

Achieved SMAPE of 7.28% on AASCE dataset

Attained MAE of 3.18 degrees, outperforming existing methods

Demonstrated robustness and efficiency in clinical assessment

Abstract

Scoliosis diagnosis and assessment depend largely on the measurement of the Cobb angle in spine X-ray images. With the emergence of deep learning techniques that employ landmark detection, tilt prediction, and spine segmentation, automated Cobb angle measurement has become increasingly popular. However, these methods encounter difficulties such as high noise sensitivity, intricate computational procedures, and exclusive reliance on a single type of morphological information. In this paper, we introduce the Multiple Morphology-Aware Network (MMA-Net), a novel framework that improves Cobb angle measurement accuracy by integrating multiple spine morphology as attention information. In the MMA-Net, we first feed spine X-ray images into the segmentation network to produce multiple morphological information (spine region, centerline, and boundary) and then concatenate the original X-ray image with the resulting segmentation maps as input for the regression module to perform precise Cobb angle measurement. Furthermore, we devise joint loss functions for our segmentation and regression network training, respectively. We evaluate our method on the AASCE challenge dataset and achieve superior performance with the SMAPE of 7.28% and the MAE of 3.18{\deg}, indicating a strong competitiveness compared to other outstanding methods. Consequently, we can offer clinicians automated, efficient, and reliable Cobb angle measurement.