# 3D Spine Shape Estimation from Single 2D DXA

arXiv: https://arxiv.org/abs/2412.01504 · HTML: https://mlsanity.com/paper/2412.01504/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Bottleneck Transformers for Visual Recognition](https://mlsanity.com/paper/2101.11605/) (2021)
- [A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI](https://mlsanity.com/paper/2007.02606/) (2020)
- [Self-Supervised Multi-Modal Alignment for Whole Body Medical Imaging](https://mlsanity.com/paper/2107.06652/) (2021)
- [Predicting Spine Geometry and Scoliosis from DXA Scans](https://mlsanity.com/paper/2311.09424/) (2023)

## Abstract (arXiv)

Scoliosis is traditionally assessed based solely on 2D lateral deviations, but recent studies have also revealed the importance of other imaging planes in understanding the deformation of the spine. Consequently, extracting the spinal geometry in 3D would help quantify these spinal deformations and aid diagnosis. In this study, we propose an automated general framework to estimate the 3D spine shape from 2D DXA scans. We achieve this by explicitly predicting the sagittal view of the spine from the DXA scan. Using these two orthogonal projections of the spine (coronal in DXA, and sagittal from the prediction), we are able to describe the 3D shape of the spine. The prediction is learnt from over 30k paired images of DXA and MRI scans. We assess the performance of the method on a held out test set, and achieve high accuracy.