# Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression

arXiv: https://arxiv.org/abs/1910.14202 · HTML: https://mlsanity.com/paper/1910.14202/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation](https://mlsanity.com/paper/2211.14122/) (2022)
- [Scoliosis Detection using Deep Neural Network](https://mlsanity.com/paper/2210.17269/) (2022)
- [VertXNet: An Ensemble Method for Vertebrae Segmentation and Identification of Spinal X-Ray](https://mlsanity.com/paper/2302.03476/) (2023)
- [Deep learning automates Cobb angle measurement compared with multi-expert observers](https://mlsanity.com/paper/2403.12115/) (2024)
- [GUI-based Pedicle Screw Planning on Fluoroscopic Images Utilizing Vertebral Segmentation](https://mlsanity.com/paper/2407.08347/) (2024)
- [Spine Vision X-Ray Image based GUI Planning of Pedicle Screws Using Enhanced YOLOv5 for Vertebrae Segmentation](https://mlsanity.com/paper/2407.08349/) (2024)
- [CurvNet: Latent Contour Representation and Iterative Data Engine for Curvature Angle Estimation](https://mlsanity.com/paper/2411.12604/) (2024)
- [Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine](https://mlsanity.com/paper/2508.14708/) (2025)

## Abstract (arXiv)

Correct evaluation and treatment of Scoliosis require accurate estimation of spinal curvature. Current gold standard is to manually estimate Cobb Angles in spinal X-ray images which is time consuming and has high inter-rater variability. We propose an automatic method with a novel framework that first detects vertebrae as objects followed by a landmark detector that estimates the 4 landmark corners of each vertebra separately. Cobb Angles are calculated using the slope of each vertebra obtained from the predicted landmarks. For inference on test data, we perform pre and post processings that include cropping, outlier rejection and smoothing of the predicted landmarks. The results were assessed in AASCE MICCAI challenge 2019 which showed a promise with a SMAPE score of 25.69 on the challenge test set.