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A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling

Background: Craniosynostosis is a condition caused by the premature fusion of skull sutures, leading to irregular growth patterns of the head. Three-dimensional photogrammetry is a radiation-free alternative to the diagnosis using computed tomography. While statistical shape models have been propose...

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Autores principales: Schaufelberger, Matthias, Kühle, Reinald, Wachter, Andreas, Weichel, Frederic, Hagen, Niclas, Ringwald, Friedemann, Eisenmann, Urs, Hoffmann, Jürgen, Engel, Michael, Freudlsperger, Christian, Nahm, Werner
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323148/
https://www.ncbi.nlm.nih.gov/pubmed/35885422
http://dx.doi.org/10.3390/diagnostics12071516
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author Schaufelberger, Matthias
Kühle, Reinald
Wachter, Andreas
Weichel, Frederic
Hagen, Niclas
Ringwald, Friedemann
Eisenmann, Urs
Hoffmann, Jürgen
Engel, Michael
Freudlsperger, Christian
Nahm, Werner
author_facet Schaufelberger, Matthias
Kühle, Reinald
Wachter, Andreas
Weichel, Frederic
Hagen, Niclas
Ringwald, Friedemann
Eisenmann, Urs
Hoffmann, Jürgen
Engel, Michael
Freudlsperger, Christian
Nahm, Werner
author_sort Schaufelberger, Matthias
collection PubMed
description Background: Craniosynostosis is a condition caused by the premature fusion of skull sutures, leading to irregular growth patterns of the head. Three-dimensional photogrammetry is a radiation-free alternative to the diagnosis using computed tomography. While statistical shape models have been proposed to quantify head shape, no shape-model-based classification approach has been presented yet. Methods: We present a classification pipeline that enables an automated diagnosis of three types of craniosynostosis. The pipeline is based on a statistical shape model built from photogrammetric surface scans. We made the model and pathology-specific submodels publicly available, making it the first publicly available craniosynostosis-related head model, as well as the first focusing on infants younger than 1.5 years. To the best of our knowledge, we performed the largest classification study for craniosynostosis to date. Results: Our classification approach yields an accuracy of 97.8 %, comparable to other state-of-the-art methods using both computed tomography scans and stereophotogrammetry. Regarding the statistical shape model, we demonstrate that our model performs similar to other statistical shape models of the human head. Conclusion: We present a state-of-the-art shape-model-based classification approach for a radiation-free diagnosis of craniosynostosis. Our publicly available shape model enables the assessment of craniosynostosis on realistic and synthetic data.
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spelling pubmed-93231482022-07-27 A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling Schaufelberger, Matthias Kühle, Reinald Wachter, Andreas Weichel, Frederic Hagen, Niclas Ringwald, Friedemann Eisenmann, Urs Hoffmann, Jürgen Engel, Michael Freudlsperger, Christian Nahm, Werner Diagnostics (Basel) Article Background: Craniosynostosis is a condition caused by the premature fusion of skull sutures, leading to irregular growth patterns of the head. Three-dimensional photogrammetry is a radiation-free alternative to the diagnosis using computed tomography. While statistical shape models have been proposed to quantify head shape, no shape-model-based classification approach has been presented yet. Methods: We present a classification pipeline that enables an automated diagnosis of three types of craniosynostosis. The pipeline is based on a statistical shape model built from photogrammetric surface scans. We made the model and pathology-specific submodels publicly available, making it the first publicly available craniosynostosis-related head model, as well as the first focusing on infants younger than 1.5 years. To the best of our knowledge, we performed the largest classification study for craniosynostosis to date. Results: Our classification approach yields an accuracy of 97.8 %, comparable to other state-of-the-art methods using both computed tomography scans and stereophotogrammetry. Regarding the statistical shape model, we demonstrate that our model performs similar to other statistical shape models of the human head. Conclusion: We present a state-of-the-art shape-model-based classification approach for a radiation-free diagnosis of craniosynostosis. Our publicly available shape model enables the assessment of craniosynostosis on realistic and synthetic data. MDPI 2022-06-21 /pmc/articles/PMC9323148/ /pubmed/35885422 http://dx.doi.org/10.3390/diagnostics12071516 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Schaufelberger, Matthias
Kühle, Reinald
Wachter, Andreas
Weichel, Frederic
Hagen, Niclas
Ringwald, Friedemann
Eisenmann, Urs
Hoffmann, Jürgen
Engel, Michael
Freudlsperger, Christian
Nahm, Werner
A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling
title A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling
title_full A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling
title_fullStr A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling
title_full_unstemmed A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling
title_short A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling
title_sort radiation-free classification pipeline for craniosynostosis using statistical shape modeling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323148/
https://www.ncbi.nlm.nih.gov/pubmed/35885422
http://dx.doi.org/10.3390/diagnostics12071516
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