# Robust vertebra identification using simultaneous node and edge predicting Graph Neural Networks

arXiv: https://arxiv.org/abs/2308.02509 · HTML: https://mlsanity.com/paper/2308.02509/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 27 Jul 2023.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Neural Relational Inference for Interacting Systems](https://mlsanity.com/paper/1802.04687/) (2018)
- [Learning a Neural Solver for Multiple Object Tracking](https://mlsanity.com/paper/1912.07515/) (2019)
- [Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization](https://mlsanity.com/paper/2101.11075/) (2021)
- [Iterative fully convolutional neural networks for automatic vertebra segmentation and identification](https://mlsanity.com/paper/1804.04383/) (2018)
- [Pomegranate: fast and flexible probabilistic modeling in python](https://mlsanity.com/paper/1711.00137/) (2017)
- [Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization](https://mlsanity.com/paper/1705.05998/) (2017)
- [Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle](https://mlsanity.com/paper/2110.12177/) (2021)
- [Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information](https://mlsanity.com/paper/1812.03500/) (2018)
- [Beyond Node Embedding: A Direct Unsupervised Edge Representation Framework for Homogeneous Networks](https://mlsanity.com/paper/1912.05140/) (2019)
- [Learning Spatial Context with Graph Neural Network for Multi-Person Pose Grouping](https://mlsanity.com/paper/2104.02385/) (2021)

## Built on by (most cited first, 3 of 3)

- [Explainable vertebral fracture analysis with uncertainty estimation using differentiable rule-based classification](https://mlsanity.com/paper/2407.02926/) (2024)
- [Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding](https://mlsanity.com/paper/2312.05220/) (2023)
- [Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine](https://mlsanity.com/paper/2508.14708/) (2025)

## Abstract (arXiv)

Automatic vertebra localization and identification in CT scans is important for numerous clinical applications. Much progress has been made on this topic, but it mostly targets positional localization of vertebrae, ignoring their orientation. Additionally, most methods employ heuristics in their pipeline that can be sensitive in real clinical images which tend to contain abnormalities. We introduce a simple pipeline that employs a standard prediction with a U-Net, followed by a single graph neural network to associate and classify vertebrae with full orientation. To test our method, we introduce a new vertebra dataset that also contains pedicle detections that are associated with vertebra bodies, creating a more challenging landmark prediction, association and classification task. Our method is able to accurately associate the correct body and pedicle landmarks, ignore false positives and classify vertebrae in a simple, fully trainable pipeline avoiding application-specific heuristics. We show our method outperforms traditional approaches such as Hungarian Matching and Hidden Markov Models. We also show competitive performance on the standard VerSe challenge body identification task.