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-...
Autores principales: | , , , |
---|---|
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 |