  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.
