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Article

Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs †

1
Department of Computer Science, Technical University of Munich, 85748 Garching, Germany
2
Department of Neuroradiology, Klinikum rech der Isar, 81675 Munich, Germany
3
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
4
A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
5
Department of Quantitative Biomedicine, University of Zurich, 8006 Zurich, Switzerland
6
Institute of Computer Graphics and Vision, Graz University of Technology, 8010 Graz, Austria
7
School of Computer Science, University of Auckland, Auckland 1010, New Zealand
*
Author to whom correspondence should be addressed.
This paper is an extended version of our conference paper: Bayat, A.; Sekuboyina, A.; Paetzold, J.C.; Payer, C.; Stern, D.; Urschler, M.; Kirschke, J.S.; Menze, B.H. Inferring the 3D standing spine posture from 2D radiographs. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru, 4–8 October 2020; pp. 775–784.
These authors contributed equally to this work.
Academic Editor: Emilio Quaia
Tomography 2022, 8(1), 479-496; https://doi.org/10.3390/tomography8010039
Received: 6 December 2021 / Revised: 30 January 2022 / Accepted: 3 February 2022 / Published: 11 February 2022
An important factor for the development of spinal degeneration, pain and the outcome of spinal surgery is known to be the balance of the spine. It must be analyzed in an upright, standing position to ensure physiological loading conditions and visualize load-dependent deformations. Despite the complex 3D shape of the spine, this analysis is currently performed using 2D radiographs, as all frequently used 3D imaging techniques require the patient to be scanned in a prone position. To overcome this limitation, we propose a deep neural network to reconstruct the 3D spinal pose in an upright standing position, loaded naturally. Specifically, we propose a novel neural network architecture, which takes orthogonal 2D radiographs and infers the spine’s 3D posture using vertebral shape priors. In this work, we define vertebral shape priors using an atlas and a spine shape prior, incorporating both into our proposed network architecture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 0.95, indicating an almost perfect 2D-to-3D domain translation. Validating the reconstruction accuracy of a 3D standing spine on real data is infeasible due to the lack of a valid ground truth. Hence, we design a novel experiment for this purpose, using an orientation invariant distance metric, to evaluate our model’s ability to synthesize full-3D, upright, and patient-specific spine models. We compare the synthesized spine shapes from clinical upright standing radiographs to the same patient’s 3D spinal posture in the prone position from CT. View Full-Text
Keywords: 3D reconstruction; shape priors; neural networks; registration; template 3D reconstruction; shape priors; neural networks; registration; template
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MDPI and ACS Style

Bayat, A.; Pace, D.F.; Sekuboyina, A.; Payer, C.; Stern, D.; Urschler, M.; Kirschke, J.S.; Menze, B.H. Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs. Tomography 2022, 8, 479-496. https://doi.org/10.3390/tomography8010039

AMA Style

Bayat A, Pace DF, Sekuboyina A, Payer C, Stern D, Urschler M, Kirschke JS, Menze BH. Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs. Tomography. 2022; 8(1):479-496. https://doi.org/10.3390/tomography8010039

Chicago/Turabian Style

Bayat, Amirhossein, Danielle F. Pace, Anjany Sekuboyina, Christian Payer, Darko Stern, Martin Urschler, Jan S. Kirschke, and Bjoern H. Menze. 2022. "Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs" Tomography 8, no. 1: 479-496. https://doi.org/10.3390/tomography8010039

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