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Symptomatic spinal vertebral compression fractures (VCFs) often requireosteoplasty treatment. A cement-like material is injected into the bone tostabilize the fracture, restore the vertebral body height and alleviate pain.Leakage is a common complication and may occur due to too much cement beinginjected. In this work, we propose an automated patient-specific framework thatcan allow physicians to calculate an upper bound of cement for the injectionand estimate the optimal outcome of osteoplasty. The framework uses the patientCT scan and the fractured vertebra label to build a virtual healthy spine usinga high-level approach. Firstly, the fractured spine is segmented with athree-step Convolution Neural Network (CNN) architecture. Next, a per-vertebrarigid registration to a healthy spine atlas restores its curvature. Finally, aGAN-based inpainting approach replaces the fractured vertebra with anestimation of its original shape. Based on this outcome, we then estimate themaximum amount of bone cement for injection. We evaluate our framework bycomparing the virtual vertebrae volumes of ten patients to their healthyequivalent and report an average error of 3.88$\pm$7.63\%. The presentedpipeline offers a first approach to a personalized automatic high-levelframework for planning osteoplasty procedures.

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