Cargando…
A semi-automatic seed point-based method for separation of individual vertebrae in 3D surface meshes: a proof of principle study
PURPOSE: The purpose of this paper is to present and validate a new semi-automated 3D surface mesh segmentation approach that optimizes the laborious individual human vertebrae separation in the spinal virtual surgical planning workflow and make a direct accuracy and segmentation time comparison wit...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354998/ https://www.ncbi.nlm.nih.gov/pubmed/34043144 http://dx.doi.org/10.1007/s11548-021-02407-z |
_version_ | 1783736696686247936 |
---|---|
author | Pijpker, Peter A. J. Oosterhuis, Tim S. Witjes, Max J. H. Faber, Chris van Ooijen, Peter M. A. Kosinka, Jiří Kuijlen, Jos M. A. Groen, Rob J. M. Kraeima, Joep |
author_facet | Pijpker, Peter A. J. Oosterhuis, Tim S. Witjes, Max J. H. Faber, Chris van Ooijen, Peter M. A. Kosinka, Jiří Kuijlen, Jos M. A. Groen, Rob J. M. Kraeima, Joep |
author_sort | Pijpker, Peter A. J. |
collection | PubMed |
description | PURPOSE: The purpose of this paper is to present and validate a new semi-automated 3D surface mesh segmentation approach that optimizes the laborious individual human vertebrae separation in the spinal virtual surgical planning workflow and make a direct accuracy and segmentation time comparison with current standard segmentation method. METHODS: The proposed semi-automatic method uses the 3D bone surface derived from CT image data for seed point-based 3D mesh partitioning. The accuracy of the proposed method was evaluated on a representative patient dataset. In addition, the influence of the number of used seed points was studied. The investigators analyzed whether there was a reduction in segmentation time when compared to manual segmentation. Surface-to-surface accuracy measurements were applied to assess the concordance with the manual segmentation. RESULTS: The results demonstrated a statically significant reduction in segmentation time, while maintaining a high accuracy compared to the manual segmentation. A considerably smaller error was found when increasing the number of seed points. Anatomical regions that include articulating areas tend to show the highest errors, while the posterior laminar surface yielded an almost negligible error. CONCLUSION: A novel seed point initiated surface based segmentation method for the laborious individual human vertebrae separation was presented. This proof-of-principle study demonstrated the accuracy of the proposed method on a clinical CT image dataset and its feasibility for spinal virtual surgical planning applications. |
format | Online Article Text |
id | pubmed-8354998 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-83549982021-08-25 A semi-automatic seed point-based method for separation of individual vertebrae in 3D surface meshes: a proof of principle study Pijpker, Peter A. J. Oosterhuis, Tim S. Witjes, Max J. H. Faber, Chris van Ooijen, Peter M. A. Kosinka, Jiří Kuijlen, Jos M. A. Groen, Rob J. M. Kraeima, Joep Int J Comput Assist Radiol Surg Original Article PURPOSE: The purpose of this paper is to present and validate a new semi-automated 3D surface mesh segmentation approach that optimizes the laborious individual human vertebrae separation in the spinal virtual surgical planning workflow and make a direct accuracy and segmentation time comparison with current standard segmentation method. METHODS: The proposed semi-automatic method uses the 3D bone surface derived from CT image data for seed point-based 3D mesh partitioning. The accuracy of the proposed method was evaluated on a representative patient dataset. In addition, the influence of the number of used seed points was studied. The investigators analyzed whether there was a reduction in segmentation time when compared to manual segmentation. Surface-to-surface accuracy measurements were applied to assess the concordance with the manual segmentation. RESULTS: The results demonstrated a statically significant reduction in segmentation time, while maintaining a high accuracy compared to the manual segmentation. A considerably smaller error was found when increasing the number of seed points. Anatomical regions that include articulating areas tend to show the highest errors, while the posterior laminar surface yielded an almost negligible error. CONCLUSION: A novel seed point initiated surface based segmentation method for the laborious individual human vertebrae separation was presented. This proof-of-principle study demonstrated the accuracy of the proposed method on a clinical CT image dataset and its feasibility for spinal virtual surgical planning applications. Springer International Publishing 2021-05-27 2021 /pmc/articles/PMC8354998/ /pubmed/34043144 http://dx.doi.org/10.1007/s11548-021-02407-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Pijpker, Peter A. J. Oosterhuis, Tim S. Witjes, Max J. H. Faber, Chris van Ooijen, Peter M. A. Kosinka, Jiří Kuijlen, Jos M. A. Groen, Rob J. M. Kraeima, Joep A semi-automatic seed point-based method for separation of individual vertebrae in 3D surface meshes: a proof of principle study |
title | A semi-automatic seed point-based method for separation of individual vertebrae in 3D surface meshes: a proof of principle study |
title_full | A semi-automatic seed point-based method for separation of individual vertebrae in 3D surface meshes: a proof of principle study |
title_fullStr | A semi-automatic seed point-based method for separation of individual vertebrae in 3D surface meshes: a proof of principle study |
title_full_unstemmed | A semi-automatic seed point-based method for separation of individual vertebrae in 3D surface meshes: a proof of principle study |
title_short | A semi-automatic seed point-based method for separation of individual vertebrae in 3D surface meshes: a proof of principle study |
title_sort | semi-automatic seed point-based method for separation of individual vertebrae in 3d surface meshes: a proof of principle study |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354998/ https://www.ncbi.nlm.nih.gov/pubmed/34043144 http://dx.doi.org/10.1007/s11548-021-02407-z |
work_keys_str_mv | AT pijpkerpeteraj asemiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT oosterhuistims asemiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT witjesmaxjh asemiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT faberchris asemiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT vanooijenpeterma asemiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT kosinkajiri asemiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT kuijlenjosma asemiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT groenrobjm asemiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT kraeimajoep asemiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT pijpkerpeteraj semiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT oosterhuistims semiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT witjesmaxjh semiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT faberchris semiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT vanooijenpeterma semiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT kosinkajiri semiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT kuijlenjosma semiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT groenrobjm semiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy AT kraeimajoep semiautomaticseedpointbasedmethodforseparationofindividualvertebraein3dsurfacemeshesaproofofprinciplestudy |