Symptomatic spinal vertebral compression fractures are often treated by osteoplasty where 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. Here, we propose an automated patient-specific framework that can allow physicians to calculate an upper bound of the volume of cement for particular types of VCFs and estimate the optimal outcome of osteoplasty. The framework uses the patient CT scan and the segmentation label of the fractured vertebra to build a virtual healthy spine. Firstly, the fractured spine is segmented with a three-step Convolutional Neural Network architecture. Next, a per-vertebra rigid registration to a healthy reference spine restores its curvature. Finally, a GAN-based inpainting approach replaces the fractured vertebra with an estimation of its original shape, the volume of which we use as an estimate of the original healthy vertebra volume. As a clinical application, we derive an upper bound on the amount of bone cement for the injection. We evaluate our framework by comparing the virtual vertebrae volumes of ten patients to their healthy equivalent and report an error of 3.88 \textpm 7.63%. The presented pipeline offers a first approach to a personalized automatic high-level framework for planning osteoplasty procedures.     «

Symptomatic spinal vertebral compression fractures are often treated by osteoplasty where 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. Here, we propose an automated patient-specific framework that can allow physicians to calculate an upper bound of the volume of cement for particular types of VCFs and estimate the optimal outco...     »