Cargando…

Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software

Medical image segmentation, whether semi-automatically or manually, is labor-intensive, subjective, and needs specialized personnel. The fully automated segmentation process recently gained importance due to its better design and understanding of CNNs. Considering this, we decided to develop our in-...

Descripción completa

Detalles Bibliográficos
Autores principales: Ileșan, Robert R., Beyer, Michel, Kunz, Christoph, Thieringer, Florian M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10215609/
https://www.ncbi.nlm.nih.gov/pubmed/37237673
http://dx.doi.org/10.3390/bioengineering10050604
_version_ 1785048104207122432
author Ileșan, Robert R.
Beyer, Michel
Kunz, Christoph
Thieringer, Florian M.
author_facet Ileșan, Robert R.
Beyer, Michel
Kunz, Christoph
Thieringer, Florian M.
author_sort Ileșan, Robert R.
collection PubMed
description Medical image segmentation, whether semi-automatically or manually, is labor-intensive, subjective, and needs specialized personnel. The fully automated segmentation process recently gained importance due to its better design and understanding of CNNs. Considering this, we decided to develop our in-house segmentation software and compare it to the systems of established companies, an inexperienced user, and an expert as ground truth. The companies included in the study have a cloud-based option that performs accurately in clinical routine (dice similarity coefficient of 0.912 to 0.949) with an average segmentation time ranging from 3′54″ to 85′54″. Our in-house model achieved an accuracy of 94.24% compared to the best-performing software and had the shortest mean segmentation time of 2′03″. During the study, developing in-house segmentation software gave us a glimpse into the strenuous work that companies face when offering clinically relevant solutions. All the problems encountered were discussed with the companies and solved, so both parties benefited from this experience. In doing so, we demonstrated that fully automated segmentation needs further research and collaboration between academics and the private sector to achieve full acceptance in clinical routines.
format Online
Article
Text
id pubmed-10215609
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-102156092023-05-27 Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software Ileșan, Robert R. Beyer, Michel Kunz, Christoph Thieringer, Florian M. Bioengineering (Basel) Article Medical image segmentation, whether semi-automatically or manually, is labor-intensive, subjective, and needs specialized personnel. The fully automated segmentation process recently gained importance due to its better design and understanding of CNNs. Considering this, we decided to develop our in-house segmentation software and compare it to the systems of established companies, an inexperienced user, and an expert as ground truth. The companies included in the study have a cloud-based option that performs accurately in clinical routine (dice similarity coefficient of 0.912 to 0.949) with an average segmentation time ranging from 3′54″ to 85′54″. Our in-house model achieved an accuracy of 94.24% compared to the best-performing software and had the shortest mean segmentation time of 2′03″. During the study, developing in-house segmentation software gave us a glimpse into the strenuous work that companies face when offering clinically relevant solutions. All the problems encountered were discussed with the companies and solved, so both parties benefited from this experience. In doing so, we demonstrated that fully automated segmentation needs further research and collaboration between academics and the private sector to achieve full acceptance in clinical routines. MDPI 2023-05-17 /pmc/articles/PMC10215609/ /pubmed/37237673 http://dx.doi.org/10.3390/bioengineering10050604 Text en © 2023 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
Ileșan, Robert R.
Beyer, Michel
Kunz, Christoph
Thieringer, Florian M.
Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software
title Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software
title_full Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software
title_fullStr Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software
title_full_unstemmed Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software
title_short Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software
title_sort comparison of artificial intelligence-based applications for mandible segmentation: from established platforms to in-house-developed software
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10215609/
https://www.ncbi.nlm.nih.gov/pubmed/37237673
http://dx.doi.org/10.3390/bioengineering10050604
work_keys_str_mv AT ilesanrobertr comparisonofartificialintelligencebasedapplicationsformandiblesegmentationfromestablishedplatformstoinhousedevelopedsoftware
AT beyermichel comparisonofartificialintelligencebasedapplicationsformandiblesegmentationfromestablishedplatformstoinhousedevelopedsoftware
AT kunzchristoph comparisonofartificialintelligencebasedapplicationsformandiblesegmentationfromestablishedplatformstoinhousedevelopedsoftware
AT thieringerflorianm comparisonofartificialintelligencebasedapplicationsformandiblesegmentationfromestablishedplatformstoinhousedevelopedsoftware