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-...
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Autor*in:	Robert R. Ileșan [verfasserIn] Michel Beyer [verfasserIn] Christoph Kunz [verfasserIn] Florian M. Thieringer [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	artificial intelligence mandible segmentation 3D virtual reconstruction CBCT CT

Übergeordnetes Werk:	In: Bioengineering - MDPI AG, 2014, 10(2023), 5, p 604
Übergeordnetes Werk:	volume:10 ; year:2023 ; number:5, p 604

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/bioengineering10050604

Katalog-ID:	DOAJ09441713X

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language	English
source	In Bioengineering 10(2023), 5, p 604 volume:10 year:2023 number:5, p 604
sourceStr	In Bioengineering 10(2023), 5, p 604 volume:10 year:2023 number:5, p 604
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authorswithroles_txt_mv	Robert R. Ileșan @@aut@@ Michel Beyer @@aut@@ Christoph Kunz @@aut@@ Florian M. Thieringer @@aut@@
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author	Robert R. Ileșan
spellingShingle	Robert R. Ileșan misc QH301-705.5 misc artificial intelligence misc mandible misc segmentation misc 3D virtual reconstruction misc CBCT misc CT misc Technology misc T misc Biology (General) Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software
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topic_title	QH301-705.5 Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software artificial intelligence mandible segmentation 3D virtual reconstruction CBCT CT
topic	misc QH301-705.5 misc artificial intelligence misc mandible misc segmentation misc 3D virtual reconstruction misc CBCT misc CT misc Technology misc T misc Biology (General)
topic_unstemmed	misc QH301-705.5 misc artificial intelligence misc mandible misc segmentation misc 3D virtual reconstruction misc CBCT misc CT misc Technology misc T misc Biology (General)
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title	Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software
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title_sort	comparison of artificial intelligence-based applications for mandible segmentation: from established platforms to in-house-developed software
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title_auth	Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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