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Scoliosis diagnosis and assessment depend largely on the measurement of theCobb angle in spine X-ray images. With the emergence of deep learningtechniques that employ landmark detection, tilt prediction, and spinesegmentation, 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 ofmorphological information. In this paper, we introduce the MultipleMorphology-Aware Network (MMA-Net), a novel framework that improves Cobb anglemeasurement accuracy by integrating multiple spine morphology as attentioninformation. In the MMA-Net, we first feed spine X-ray images into thesegmentation network to produce multiple morphological information (spineregion, centerline, and boundary) and then concatenate the original X-ray imagewith the resulting segmentation maps as input for the regression module toperform precise Cobb angle measurement. Furthermore, we devise joint lossfunctions for our segmentation and regression network training, respectively.We evaluate our method on the AASCE challenge dataset and achieve superiorperformance with the SMAPE of 7.28% and the MAE of 3.18{\deg}, indicating astrong competitiveness compared to other outstanding methods. Consequently, wecan offer clinicians automated, efficient, and reliable Cobb angle measurement.

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