Cargando…

Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks

PURPOSE: Computer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head C...

Descripción completa

Detalles Bibliográficos
Autores principales: Steybe, David, Poxleitner, Philipp, Metzger, Marc Christian, Brandenburg, Leonard Simon, Schmelzeisen, Rainer, Bamberg, Fabian, Tran, Phuong Hien, Kellner, Elias, Reisert, Marco, Russe, Maximilian Frederik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9515026/
https://www.ncbi.nlm.nih.gov/pubmed/35665881
http://dx.doi.org/10.1007/s11548-022-02673-5
_version_ 1784798400715161600
author Steybe, David
Poxleitner, Philipp
Metzger, Marc Christian
Brandenburg, Leonard Simon
Schmelzeisen, Rainer
Bamberg, Fabian
Tran, Phuong Hien
Kellner, Elias
Reisert, Marco
Russe, Maximilian Frederik
author_facet Steybe, David
Poxleitner, Philipp
Metzger, Marc Christian
Brandenburg, Leonard Simon
Schmelzeisen, Rainer
Bamberg, Fabian
Tran, Phuong Hien
Kellner, Elias
Reisert, Marco
Russe, Maximilian Frederik
author_sort Steybe, David
collection PubMed
description PURPOSE: Computer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head CT scans. METHODS: The deep learning approach of this study was based on the patchwork toolbox, using a multiscale stack of 3D convolutional neural networks. The images were split into nested patches using a fixed 3D matrix size with decreasing physical size in a pyramid format of four scale depths. Manual segmentation of 18 craniomaxillofacial structures was performed in 20 CT scans, of which 15 were used for the training of the deep learning network and five were used for validation of the results of automated segmentation. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC), surface DSC, 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD). RESULTS: Mean for DSC was 0.81 ± 0.13 (range: 0.61 [mental foramen] – 0.98 [mandible]). Mean Surface DSC was 0.94 ± 0.06 (range: 0.87 [mental foramen] – 0.99 [mandible]), with values > 0.9 for all structures but the mental foramen. Mean 95HD was 1.93 ± 2.05 mm (range: 1.00 [mandible] – 4.12 mm [maxillary sinus]) and for ASSD, a mean of 0.42 ± 0.44 mm (range: 0.09 [mandible] – 1.19 mm [mental foramen]) was found, with values < 1 mm for all structures but the mental foramen. CONCLUSION: In this study, high accuracy of automated segmentation of a variety of craniomaxillofacial structures could be demonstrated, suggesting this approach to be suitable for the incorporation into a computer-assisted craniomaxillofacial surgery workflow. The small amount of training data required and the flexibility of an open source-based network architecture enable a broad variety of clinical and research applications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11548-022-02673-5.
format Online
Article
Text
id pubmed-9515026
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Springer International Publishing
record_format MEDLINE/PubMed
spelling pubmed-95150262022-09-29 Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks Steybe, David Poxleitner, Philipp Metzger, Marc Christian Brandenburg, Leonard Simon Schmelzeisen, Rainer Bamberg, Fabian Tran, Phuong Hien Kellner, Elias Reisert, Marco Russe, Maximilian Frederik Int J Comput Assist Radiol Surg Original Article PURPOSE: Computer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head CT scans. METHODS: The deep learning approach of this study was based on the patchwork toolbox, using a multiscale stack of 3D convolutional neural networks. The images were split into nested patches using a fixed 3D matrix size with decreasing physical size in a pyramid format of four scale depths. Manual segmentation of 18 craniomaxillofacial structures was performed in 20 CT scans, of which 15 were used for the training of the deep learning network and five were used for validation of the results of automated segmentation. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC), surface DSC, 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD). RESULTS: Mean for DSC was 0.81 ± 0.13 (range: 0.61 [mental foramen] – 0.98 [mandible]). Mean Surface DSC was 0.94 ± 0.06 (range: 0.87 [mental foramen] – 0.99 [mandible]), with values > 0.9 for all structures but the mental foramen. Mean 95HD was 1.93 ± 2.05 mm (range: 1.00 [mandible] – 4.12 mm [maxillary sinus]) and for ASSD, a mean of 0.42 ± 0.44 mm (range: 0.09 [mandible] – 1.19 mm [mental foramen]) was found, with values < 1 mm for all structures but the mental foramen. CONCLUSION: In this study, high accuracy of automated segmentation of a variety of craniomaxillofacial structures could be demonstrated, suggesting this approach to be suitable for the incorporation into a computer-assisted craniomaxillofacial surgery workflow. The small amount of training data required and the flexibility of an open source-based network architecture enable a broad variety of clinical and research applications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11548-022-02673-5. Springer International Publishing 2022-06-03 2022 /pmc/articles/PMC9515026/ /pubmed/35665881 http://dx.doi.org/10.1007/s11548-022-02673-5 Text en © The Author(s) 2022 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
Steybe, David
Poxleitner, Philipp
Metzger, Marc Christian
Brandenburg, Leonard Simon
Schmelzeisen, Rainer
Bamberg, Fabian
Tran, Phuong Hien
Kellner, Elias
Reisert, Marco
Russe, Maximilian Frederik
Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks
title Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks
title_full Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks
title_fullStr Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks
title_full_unstemmed Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks
title_short Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks
title_sort automated segmentation of head ct scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9515026/
https://www.ncbi.nlm.nih.gov/pubmed/35665881
http://dx.doi.org/10.1007/s11548-022-02673-5
work_keys_str_mv AT steybedavid automatedsegmentationofheadctscansforcomputerassistedcraniomaxillofacialsurgeryapplyingahierarchicalpatchbasedstackofconvolutionalneuralnetworks
AT poxleitnerphilipp automatedsegmentationofheadctscansforcomputerassistedcraniomaxillofacialsurgeryapplyingahierarchicalpatchbasedstackofconvolutionalneuralnetworks
AT metzgermarcchristian automatedsegmentationofheadctscansforcomputerassistedcraniomaxillofacialsurgeryapplyingahierarchicalpatchbasedstackofconvolutionalneuralnetworks
AT brandenburgleonardsimon automatedsegmentationofheadctscansforcomputerassistedcraniomaxillofacialsurgeryapplyingahierarchicalpatchbasedstackofconvolutionalneuralnetworks
AT schmelzeisenrainer automatedsegmentationofheadctscansforcomputerassistedcraniomaxillofacialsurgeryapplyingahierarchicalpatchbasedstackofconvolutionalneuralnetworks
AT bambergfabian automatedsegmentationofheadctscansforcomputerassistedcraniomaxillofacialsurgeryapplyingahierarchicalpatchbasedstackofconvolutionalneuralnetworks
AT tranphuonghien automatedsegmentationofheadctscansforcomputerassistedcraniomaxillofacialsurgeryapplyingahierarchicalpatchbasedstackofconvolutionalneuralnetworks
AT kellnerelias automatedsegmentationofheadctscansforcomputerassistedcraniomaxillofacialsurgeryapplyingahierarchicalpatchbasedstackofconvolutionalneuralnetworks
AT reisertmarco automatedsegmentationofheadctscansforcomputerassistedcraniomaxillofacialsurgeryapplyingahierarchicalpatchbasedstackofconvolutionalneuralnetworks
AT russemaximilianfrederik automatedsegmentationofheadctscansforcomputerassistedcraniomaxillofacialsurgeryapplyingahierarchicalpatchbasedstackofconvolutionalneuralnetworks