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

arXiv: https://arxiv.org/abs/2309.13817 · HTML: https://mlsanity.com/paper/2309.13817/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer](https://mlsanity.com/paper/2109.04335/) (2021)
- [Boundary Loss for Remote Sensing Imagery Semantic Segmentation](https://mlsanity.com/paper/1905.07852/) (2019)
- [Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression](https://mlsanity.com/paper/2208.12462/) (2022)

## Built on by (most cited first, 1 of 1)

- [Deep learning automates Cobb angle measurement compared with multi-expert observers](https://mlsanity.com/paper/2403.12115/) (2024)

## Abstract (arXiv)

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 3.18deg, indicating a strong competitiveness compared to other outstanding methods. Consequently, we can offer clinicians automated, efficient, and reliable Cobb angle measurement.