# Tracing 3D Anatomy in 2D Strokes: A Multi-Stage Projection Driven Approach to Cervical Spine Fracture Identification

arXiv: https://arxiv.org/abs/2601.15235 · HTML: https://mlsanity.com/paper/2601.15235/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 81 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
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- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [A Multiscale Visualization of Attention in the Transformer Model](https://mlsanity.com/paper/1906.05714/) (2019)
- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography](https://mlsanity.com/paper/2105.14711/) (2021)
- [Iterative fully convolutional neural networks for automatic vertebra segmentation and identification](https://mlsanity.com/paper/1804.04383/) (2018)
- [Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks](https://mlsanity.com/paper/1707.00478/) (2017)
- [Detection of vertebral fractures in CT using 3D Convolutional Neural Networks](https://mlsanity.com/paper/1911.01816/) (2019)
- [Deep Sequential Learning for Cervical Spine Fracture Detection on Computed Tomography Imaging](https://mlsanity.com/paper/2010.13336/) (2020)
- [Atrous Residual Interconnected Encoder to Attention Decoder Framework for Vertebrae Segmentation via 3D Volumetric CT Images](https://mlsanity.com/paper/2104.03715/) (2021)

## Abstract (arXiv)

Cervical spine fractures require rapid and accurate diagnosis, yet automatic CT interpretation remains challenging as subtle injuries must be assessed across large 3D volumes. We ask whether full 3D vertebra segmentation is necessary for automated fracture recognition, or whether vertebra masks approximated from 2D projections can preserve sufficient diagnostic context. We propose an end-to-end pipeline that localizes the cervical spine, estimates C1-C7 vertebra masks from optimized 2D projections, and uses the resulting vertebra-level volumes for downstream fracture classification. A YOLOv8 detector first localizes spine regions of interest from multi-view variance projections, achieving a 3D mean Intersection over Union of 94.45%. Multi-label vertebra segmentation is then performed with a DenseNet121-Unet on energy-based sagittal and coronal projections, attaining a mean Dice score of 87.86%. The predicted 2D masks are back-projected and fused into approximate 3D masks for each vertebra to extract volumes of interest from the original CT. These volumes are analyzed by an ensemble of 2.5D spatio-sequential CNN-Transformer models, yielding vertebra-level and patient-level F1 scores of 68.15 and 82.26, area under the receiver operating characteristic curve of 91.62 and 90.95, and area under the precision-recall curve of 75.60 and 92.00, respectively. The projection-derived volumes achieved fracture-recognition performance comparable to a full 3D-segmentation baseline, while shifting the vertebra segmentation stage into a lower-dimensional domain. Saliency-based explainability and interobserver variability analysis further examine interpretability and reliability. Overall, the results indicate that projection-based mask approximation is a viable proxy for full 3D vertebra segmentation in cervical fracture recognition.