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Automatic generation of subject-specific finite element models of the spine from magnetic resonance images
The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10508183/ https://www.ncbi.nlm.nih.gov/pubmed/37731762 http://dx.doi.org/10.3389/fbioe.2023.1244291 |
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author | Kok, Joeri Shcherbakova, Yulia M. Schlösser, Tom P. C. Seevinck, Peter R. van der Velden, Tijl A. Castelein, René M. Ito, Keita van Rietbergen, Bert |
author_facet | Kok, Joeri Shcherbakova, Yulia M. Schlösser, Tom P. C. Seevinck, Peter R. van der Velden, Tijl A. Castelein, René M. Ito, Keita van Rietbergen, Bert |
author_sort | Kok, Joeri |
collection | PubMed |
description | The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echo-gradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deep-learning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93–0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1 mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models. |
format | Online Article Text |
id | pubmed-10508183 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105081832023-09-20 Automatic generation of subject-specific finite element models of the spine from magnetic resonance images Kok, Joeri Shcherbakova, Yulia M. Schlösser, Tom P. C. Seevinck, Peter R. van der Velden, Tijl A. Castelein, René M. Ito, Keita van Rietbergen, Bert Front Bioeng Biotechnol Bioengineering and Biotechnology The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echo-gradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deep-learning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93–0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1 mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models. Frontiers Media S.A. 2023-09-05 /pmc/articles/PMC10508183/ /pubmed/37731762 http://dx.doi.org/10.3389/fbioe.2023.1244291 Text en Copyright © 2023 Kok, Shcherbakova, Schlösser, Seevinck, van der Velden, Castelein, Ito and van Rietbergen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Kok, Joeri Shcherbakova, Yulia M. Schlösser, Tom P. C. Seevinck, Peter R. van der Velden, Tijl A. Castelein, René M. Ito, Keita van Rietbergen, Bert Automatic generation of subject-specific finite element models of the spine from magnetic resonance images |
title | Automatic generation of subject-specific finite element models of the spine from magnetic resonance images |
title_full | Automatic generation of subject-specific finite element models of the spine from magnetic resonance images |
title_fullStr | Automatic generation of subject-specific finite element models of the spine from magnetic resonance images |
title_full_unstemmed | Automatic generation of subject-specific finite element models of the spine from magnetic resonance images |
title_short | Automatic generation of subject-specific finite element models of the spine from magnetic resonance images |
title_sort | automatic generation of subject-specific finite element models of the spine from magnetic resonance images |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10508183/ https://www.ncbi.nlm.nih.gov/pubmed/37731762 http://dx.doi.org/10.3389/fbioe.2023.1244291 |
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