# Inferring the 3D Standing Spine Posture from 2D Radiographs

arXiv: https://arxiv.org/abs/2007.06612 · HTML: https://mlsanity.com/paper/2007.06612/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images](https://mlsanity.com/paper/2309.08481/) (2023)
- [Accurate Patient Alignment without Unnecessary Imaging Dose via Synthesizing Patient-specific 3D CT Images from 2D kV Images](https://mlsanity.com/paper/2405.19338/) (2024)
- [Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction](https://mlsanity.com/paper/2309.13587/) (2023)

## Abstract (arXiv)

The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural weight bearing is crucial for such bio-mechanical analysis. 3D volumetric imaging modalities (e.g. CT and MRI) are performed in patients lying down. On the other hand, radiographs are captured in an upright pose, but result in 2D projections. This work aims to integrate the two realms, i.e. it combines the upright spinal curvature from radiographs with the 3D vertebral shape from CT imaging for synthesizing an upright 3D model of spine, loaded naturally. Specifically, we propose a novel neural network architecture working vertebra-wise, termed TransVert, which takes orthogonal 2D radiographs and infers the spine's 3D posture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 95.52%, indicating an almost perfect 2D-to-3D domain translation. Deploying our model on clinical radiographs, we successfully synthesise full-3D, upright, patient-specific spine models for the first time.