Cargando…
Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network
PURPOSE: For computer-aided planning of facial bony surgery, the creation of high-resolution 3D-models of the bones by segmenting volume imaging data is a labor-intensive step, especially as metal dental inlays or implants cause severe artifacts that reduce the quality of the computer-tomographic im...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10363055/ https://www.ncbi.nlm.nih.gov/pubmed/36637748 http://dx.doi.org/10.1007/s11548-022-02830-w |
_version_ | 1785076557412302848 |
---|---|
author | Pankert, Tobias Lee, Hyun Peters, Florian Hölzle, Frank Modabber, Ali Raith, Stefan |
author_facet | Pankert, Tobias Lee, Hyun Peters, Florian Hölzle, Frank Modabber, Ali Raith, Stefan |
author_sort | Pankert, Tobias |
collection | PubMed |
description | PURPOSE: For computer-aided planning of facial bony surgery, the creation of high-resolution 3D-models of the bones by segmenting volume imaging data is a labor-intensive step, especially as metal dental inlays or implants cause severe artifacts that reduce the quality of the computer-tomographic imaging data. This study provides a method to segment accurate, artifact-free 3D surface models of mandibles from CT data using convolutional neural networks. METHODS: The presented approach cascades two independently trained 3D-U-Nets to perform accurate segmentations of the mandible bone from full resolution CT images. The networks are trained in different settings using three different loss functions and a data augmentation pipeline. Training and evaluation datasets consist of manually segmented CT images from 307 dentate and edentulous individuals, partly with heavy imaging artifacts. The accuracy of the models is measured using overlap-based, surface-based and anatomical-curvature-based metrics. RESULTS: Our approach produces high-resolution segmentations of the mandibles, coping with severe imaging artifacts in the CT imaging data. The use of the two-stepped approach yields highly significant improvements to the prediction accuracies. The best models achieve a Dice coefficient of 94.824% and an average surface distance of 0.31 mm on our test dataset. CONCLUSION: The use of two cascaded U-Net allows high-resolution predictions for small regions of interest in the imaging data. The proposed method is fast and allows a user-independent image segmentation, producing objective and repeatable results that can be used in automated surgical planning procedures. |
format | Online Article Text |
id | pubmed-10363055 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-103630552023-07-24 Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network Pankert, Tobias Lee, Hyun Peters, Florian Hölzle, Frank Modabber, Ali Raith, Stefan Int J Comput Assist Radiol Surg Original Article PURPOSE: For computer-aided planning of facial bony surgery, the creation of high-resolution 3D-models of the bones by segmenting volume imaging data is a labor-intensive step, especially as metal dental inlays or implants cause severe artifacts that reduce the quality of the computer-tomographic imaging data. This study provides a method to segment accurate, artifact-free 3D surface models of mandibles from CT data using convolutional neural networks. METHODS: The presented approach cascades two independently trained 3D-U-Nets to perform accurate segmentations of the mandible bone from full resolution CT images. The networks are trained in different settings using three different loss functions and a data augmentation pipeline. Training and evaluation datasets consist of manually segmented CT images from 307 dentate and edentulous individuals, partly with heavy imaging artifacts. The accuracy of the models is measured using overlap-based, surface-based and anatomical-curvature-based metrics. RESULTS: Our approach produces high-resolution segmentations of the mandibles, coping with severe imaging artifacts in the CT imaging data. The use of the two-stepped approach yields highly significant improvements to the prediction accuracies. The best models achieve a Dice coefficient of 94.824% and an average surface distance of 0.31 mm on our test dataset. CONCLUSION: The use of two cascaded U-Net allows high-resolution predictions for small regions of interest in the imaging data. The proposed method is fast and allows a user-independent image segmentation, producing objective and repeatable results that can be used in automated surgical planning procedures. Springer International Publishing 2023-01-13 2023 /pmc/articles/PMC10363055/ /pubmed/36637748 http://dx.doi.org/10.1007/s11548-022-02830-w Text en © The Author(s) 2023 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 Pankert, Tobias Lee, Hyun Peters, Florian Hölzle, Frank Modabber, Ali Raith, Stefan Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network |
title | Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network |
title_full | Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network |
title_fullStr | Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network |
title_full_unstemmed | Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network |
title_short | Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network |
title_sort | mandible segmentation from ct data for virtual surgical planning using an augmented two-stepped convolutional neural network |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10363055/ https://www.ncbi.nlm.nih.gov/pubmed/36637748 http://dx.doi.org/10.1007/s11548-022-02830-w |
work_keys_str_mv | AT pankerttobias mandiblesegmentationfromctdataforvirtualsurgicalplanningusinganaugmentedtwosteppedconvolutionalneuralnetwork AT leehyun mandiblesegmentationfromctdataforvirtualsurgicalplanningusinganaugmentedtwosteppedconvolutionalneuralnetwork AT petersflorian mandiblesegmentationfromctdataforvirtualsurgicalplanningusinganaugmentedtwosteppedconvolutionalneuralnetwork AT holzlefrank mandiblesegmentationfromctdataforvirtualsurgicalplanningusinganaugmentedtwosteppedconvolutionalneuralnetwork AT modabberali mandiblesegmentationfromctdataforvirtualsurgicalplanningusinganaugmentedtwosteppedconvolutionalneuralnetwork AT raithstefan mandiblesegmentationfromctdataforvirtualsurgicalplanningusinganaugmentedtwosteppedconvolutionalneuralnetwork |