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

TL;DR

This paper introduces an automated, patient-specific framework that reconstructs healthy spine models from CT scans to assist in planning osteoplasty by estimating optimal cement volume and predicting post-treatment outcomes.

Contribution

It presents a novel high-level approach combining CNN, registration, and GAN techniques to simulate healthy vertebrae and optimize osteoplasty planning.

Findings

Average vertebra volume estimation error of 3.88%

Framework successfully reconstructs healthy spine models

Potential to reduce cement leakage risk

Abstract

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$\pm$7.63\%. The presented pipeline offers a first approach to a personalized automatic high-level framework for planning osteoplasty procedures.