# Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning

arXiv: https://arxiv.org/abs/2103.07279 · HTML: https://mlsanity.com/paper/2103.07279/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://mlsanity.com/paper/1803.02579/) (2018)
- [Adversarial Inpainting of Medical Image Modalities](https://mlsanity.com/paper/1810.06621/) (2018)
- [Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior](https://mlsanity.com/paper/1804.01307/) (2018)
- [Unsupervised Learnable Sinogram Inpainting Network (SIN) for Limited Angle CT reconstruction](https://mlsanity.com/paper/1811.03911/) (2018)

## Built on by (most cited first, 6 of 6)

- [Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine](https://mlsanity.com/paper/2205.07568/) (2022)
- [AI-Enhanced Virtual Reality in Medicine: A Comprehensive Survey](https://mlsanity.com/paper/2402.03093/) (2024)
- [Shape-Aware Masking for Inpainting in Medical Imaging](https://mlsanity.com/paper/2207.05787/) (2022)
- [Topology-inspired Cross-domain Network for Developmental Cervical Stenosis Quantification](https://mlsanity.com/paper/2309.06825/) (2023)
- [An inpainting approach to manipulate asymmetry in pre-operative breast images](https://mlsanity.com/paper/2502.05652/) (2025)
- [HealthiVert-GAN: A Novel Framework of Pseudo-Healthy Vertebral Image Synthesis for Interpretable Compression Fracture Grading](https://mlsanity.com/paper/2503.05990/) (2025)

## Abstract (arXiv)

Symptomatic spinal vertebral compression fractures (VCFs) often require osteoplasty treatment. A cement-like material is injected into the bone to stabilize the fracture, restore the vertebral body height and alleviate pain. Leakage is a common complication and may occur due to too much cement being injected. In this work, we propose an automated patient-specific framework that can allow physicians to calculate an upper bound of cement for the injection and estimate the optimal outcome of osteoplasty. The framework uses the patient CT scan and the fractured vertebra label to build a virtual healthy spine using a high-level approach. Firstly, the fractured spine is segmented with a three-step Convolution Neural Network (CNN) architecture. Next, a per-vertebra rigid registration to a healthy spine atlas restores its curvature. Finally, a GAN-based inpainting approach replaces the fractured vertebra with an estimation of its original shape. Based on this outcome, we then estimate the maximum amount of bone cement for injection. We evaluate our framework by comparing the virtual vertebrae volumes of ten patients to their healthy equivalent and report an average error of 3.88±7.63%. The presented pipeline offers a first approach to a personalized automatic high-level framework for planning osteoplasty procedures.