MMA-Net: Multiple Morphology-Aware Network for Automated Cobb Angle Measurement
Zhengxuan Qiu, Jie Yang, Jiankun Wang

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMedical Imaging and Analysis · Scoliosis diagnosis and treatment · Spinal Fractures and Fixation Techniques
MethodsMasked autoencoder
