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Patient-Specific Virtual Spine Straightening and Vertebra Inpainting: An Automatic Framework for Osteoplasty Planning

Bukas C, Jian B, Venegas LFR, De Benetti F, Ruehling S, Sekuboyina A, Gempt J, Kirschke JS, Piraud M, Oberreuter J, Navab N, Wendler T (2021)

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Publication Type: Conference contribution

Publication year: 2021

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12904 LNCS

Pages Range: 529-539

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Virtual, Online

ISBN: 9783030872014

DOI: 10.1007/978-3-030-87202-1_51

Abstract

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

Involved external institutions

How to cite

APA:

Bukas, C., Jian, B., Venegas, L.F.R., De Benetti, F., Ruehling, S., Sekuboyina, A.,... Wendler, T. (2021). Patient-Specific Virtual Spine Straightening and Vertebra Inpainting: An Automatic Framework for Osteoplasty Planning. In Marleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 529-539). Virtual, Online: Springer Science and Business Media Deutschland GmbH.

MLA:

Bukas, Christina, et al. "Patient-Specific Virtual Spine Straightening and Vertebra Inpainting: An Automatic Framework for Osteoplasty Planning." Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Marleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert, Springer Science and Business Media Deutschland GmbH, 2021. 529-539.

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