Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs
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 compl...
| Main Authors: | , , , , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2022
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| Online Access: | https://hdl.handle.net/1721.1/140286 |
| _version_ | 1811071486798069760 |
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| author | Bayat, Amirhossein Pace, Danielle F. Sekuboyina, Anjany Payer, Christian Stern, Darko Urschler, Martin Kirschke, Jan S. Menze, Bjoern H. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Bayat, Amirhossein Pace, Danielle F. Sekuboyina, Anjany Payer, Christian Stern, Darko Urschler, Martin Kirschke, Jan S. Menze, Bjoern H. |
| author_sort | Bayat, Amirhossein |
| collection | MIT |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.95</mn></mrow></semantics></math></inline-formula>, 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. |
| first_indexed | 2024-09-23T08:51:54Z |
| format | Article |
| id | mit-1721.1/140286 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:51:54Z |
| publishDate | 2022 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1402862024-06-07T17:35:08Z Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs Bayat, Amirhossein Pace, Danielle F. Sekuboyina, Anjany Payer, Christian Stern, Darko Urschler, Martin Kirschke, Jan S. Menze, Bjoern H. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.95</mn></mrow></semantics></math></inline-formula>, 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. 2022-02-11T16:26:49Z 2022-02-11T16:26:49Z 2022-02-11 2022-02-11T14:46:58Z Article http://purl.org/eprint/type/JournalArticle 2379-139X https://hdl.handle.net/1721.1/140286 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 8 (1): 479-496 (2022) http://dx.doi.org/10.3390/tomography8010039 Tomography Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Bayat, Amirhossein Pace, Danielle F. Sekuboyina, Anjany Payer, Christian Stern, Darko Urschler, Martin Kirschke, Jan S. Menze, Bjoern H. Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs |
| title | Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs |
| title_full | Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs |
| title_fullStr | Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs |
| title_full_unstemmed | Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs |
| title_short | Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs |
| title_sort | anatomy aware inference of the 3d standing spine posture from 2d radiographs |
| url | https://hdl.handle.net/1721.1/140286 |
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