  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.
