Fine structural human phantom in dentistry and instance tooth segmentation
In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtu...
Saved in:
| Published in | Scientific Reports Vol. 14; no. 1; pp. 12630 - 9 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer Science and Business Media LLC
02.06.2024
Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-024-63319-x |
Cover
| Abstract | In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes. |
|---|---|
| AbstractList | In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes. In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes.In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes. Abstract In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes. |
| ArticleNumber | 12630 |
| Author | Atsushi Takeya Akihiro Haga Keiichiro Watanabe |
| Author_xml | – sequence: 1 givenname: Atsushi surname: Takeya fullname: Takeya, Atsushi organization: Graduate School of Biomedical Sciences, Tokushima University – sequence: 2 givenname: Keiichiro surname: Watanabe fullname: Watanabe, Keiichiro organization: Graduate School of Biomedical Sciences, Tokushima University – sequence: 3 givenname: Akihiro surname: Haga fullname: Haga, Akihiro email: haga@tokushima-u.ac.jp organization: Graduate School of Biomedical Sciences, Tokushima University |
| BackLink | https://cir.nii.ac.jp/crid/1871992116467451008$$DView record in CiNii https://www.ncbi.nlm.nih.gov/pubmed/38824210$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kstu1TAQhiNURC_0BVigSLBgE_A99gqhipaiSmxgbTn25BxXiX2wHdS-PT5NKS0LNr7MfP_vsT3HzUGIAZrmFUbvMaLyQ2aYK9khwjpBKVbdzbPmiCDGO0IJOXi0PmxOc_ZD3VYMSfSiOaRSEkYwOmq-nvsAbS5psWVJZmq3y2xCu9uaUOLc-tA6CMVX4LY1wdVALiZYaEuMZdtm2Mw1b4qP4WXzfDRThtP7-aT5cf75-9mX7urbxeXZp6vOCopLJ4mDfhSKjM4pB2wEZSnmlo89Eo70bBBADLWcg2I9CGY5tYIMrldW4d7Qk-Zy9XXRXOtd8rNJtzoar-8CMW20ScXbCTRmlg6AkGGAGQMnlZRYcqlGa8RAbfX6uHrtlmEGZ-td6iM8MX2aCX6rN_GXxrgaEkKqw7t7hxR_LpCLnn22ME0mQFyypkhQJqToeUXf_INexyWF-lZ7iihea0OVev24pIda_vxZBcgK2BRzTjA-IBjpfW_otTd07Q191xv6poroKsoVDhtIf8_-r-rtqgrea-v3I5Y9VopgLJjoGccISfobWaHGww |
| Cites_doi | 10.1016/j.compbiomed.2020.103720 10.1111/etp.12067 10.3390/jimaging9070134 10.4329/wjr.v6.i10.794 10.1259/dmfr.20140225 10.3390/ijerph17249428 10.1038/s41467-022-29637-2 10.1259/dmfr.20150079 10.1088/1361-6560/ac7bcd 10.2196/26151 10.1002/adem.201801013 10.1016/j.ejmp.2021.07.038 10.1109/ACCESS.2019.2924262 10.1109/ACCESS.2021.3086020 10.5334/jbr-btr.662 10.1088/1361-6560/ac9174 10.1038/sj.bdj.2015.914 10.5453/jhps.45.357 10.1002/mp.13300 10.1016/j.ejmp.2023.102648 10.3389/fmedt.2021.767836 10.1597/15-036 10.1364/JOSAA.1.000612 10.1088/0031-9155/27/3/009 10.1109/TPAMI.2021.3086072 10.1007/978-3-031-43898-1_67 10.1109/ISBI45749.2020.9098542 10.1109/CVPR.2019.00653 10.1016/j.neucom.2023.126629 10.1007/s11517-023-02889-w 10.1118/1.4955438 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024 2024. The Author(s). The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2024 – notice: 2024. The Author(s). – notice: The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | RYH C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI Q9U 7X8 5PM DOA |
| DOI | 10.1038/s41598-024-63319-x |
| DatabaseName | CiNii Complete Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection ProQuest Health & Medical Collection Medical Database Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database CrossRef MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 5 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Biology Dentistry |
| EISSN | 2045-2322 |
| EndPage | 9 |
| ExternalDocumentID | oai_doaj_org_article_14c3be00a4e144ed898818589fca6b3c PMC11144222 38824210 10_1038_s41598_024_63319_x |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: JST A-STEP grantid: JPMJTM22E4 – fundername: Japan Society for the Promotion of Science grantid: 23K07084 funderid: http://dx.doi.org/10.13039/501100001691 – fundername: Japan Society for the Promotion of Science grantid: 23K07084 |
| GroupedDBID | 0R~ 4.4 53G 5VS 7X7 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD AASML ABDBF ABUWG ACGFS ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AFPKN ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M1P M2P M48 M7P M~E NAO OK1 PHGZM PHGZT PIMPY PQQKQ PROAC PSQYO RNT RNTTT RPM RYH SNYQT UKHRP 3V. 88A ACSMW AJTQC M0L AAYXX CITATION CGR CUY CVF ECM EIF NPM PJZUB PPXIY PQGLB 7XB 8FK AARCD K9. PKEHL PQEST PQUKI Q9U 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c631t-82de7f692fdd9de4fe9c315c5f706d274b6e2a3c55e947e64c53c62bd79c917a3 |
| IEDL.DBID | AAJSJ |
| ISSN | 2045-2322 |
| IngestDate | Wed Aug 27 01:28:19 EDT 2025 Thu Aug 21 18:33:40 EDT 2025 Thu Sep 04 22:04:59 EDT 2025 Wed Aug 13 08:19:39 EDT 2025 Mon Jul 21 06:02:21 EDT 2025 Tue Jul 01 01:01:46 EDT 2025 Fri Feb 21 02:39:38 EST 2025 Thu Jun 26 21:16:12 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | 2024. The Author(s). Open Access This 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/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c631t-82de7f692fdd9de4fe9c315c5f706d274b6e2a3c55e947e64c53c62bd79c917a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.nature.com/articles/s41598-024-63319-x |
| PMID | 38824210 |
| PQID | 3062958180 |
| PQPubID | 2041939 |
| PageCount | 9 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_14c3be00a4e144ed898818589fca6b3c pubmedcentral_primary_oai_pubmedcentral_nih_gov_11144222 proquest_miscellaneous_3063468675 proquest_journals_3062958180 pubmed_primary_38824210 crossref_primary_10_1038_s41598_024_63319_x springer_journals_10_1038_s41598_024_63319_x nii_cinii_1871992116467451008 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2024-06-02 |
| PublicationDateYYYYMMDD | 2024-06-02 |
| PublicationDate_xml | – month: 06 year: 2024 text: 2024-06-02 day: 02 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Scientific Reports |
| PublicationTitleAbbrev | Sci Rep |
| PublicationTitleAlternate | Sci Rep |
| PublicationYear | 2024 |
| Publisher | Springer Science and Business Media LLC Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Springer Science and Business Media LLC – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | Zhu (CR24) 2019; 46.2 Jacobs (CR4) 2011; 94 Impellizzeri (CR8) 2020; 17.24 CR18 CR17 Angelone (CR2) 2023; 97 Lin (CR10) 2019; 21.6 CR15 Tian (CR19) 2019; 7 Abadi (CR30) 2018; 38.6 Wu, FitzGerald, Zhang, Segars, Yu, Xu, De Man (CR37) 2022; 67.19 CR35 CR12 Shimomura (CR23) 2023; 113 CR34 CR11 CR32 Nikolov (CR25) 2021; 23.7 Li (CR27) 2021; 89 Zou (CR14) 2017; 8.4 Chalmers (CR3) 2016; 53 Fujiwara (CR26) 2022; 67.15 Vallaeys (CR13) 2015; 44 Horner (CR5) 2015; 44.1 CR7 CR29 Dawood (CR9) 2015; 219.11 CR28 Todd (CR6) 2014; 31 Feldkamp, Davis, Kress (CR31) 1984; 1 CR20 Zankl (CR22) 2010; 45 Chung (CR16) 2020; 120 Cui, Fang, Mei, Zhang, Yu, Liu, Jiang, Sun, Ma, Huang, Liu, Zhao, Lian, Ding, Zhu, Shen (CR36) 2022; 13 Shah, Bansal, Logani (CR1) 2014; 6 Siddique (CR33) 2021; 9 Luo (CR21) 2024; 3 F Angelone (63319_CR2) 2023; 97 63319_CR32 S Nikolov (63319_CR25) 2021; 23.7 R Jacobs (63319_CR4) 2011; 94 M Chung (63319_CR16) 2020; 120 A Impellizzeri (63319_CR8) 2020; 17.24 M Zankl (63319_CR22) 2010; 45 LA Feldkamp (63319_CR31) 1984; 1 63319_CR35 63319_CR12 63319_CR34 63319_CR11 Z Cui (63319_CR36) 2022; 13 63319_CR18 D Fujiwara (63319_CR26) 2022; 67.15 N Shah (63319_CR1) 2014; 6 K Horner (63319_CR5) 2015; 44.1 K Vallaeys (63319_CR13) 2015; 44 63319_CR17 63319_CR15 D Luo (63319_CR21) 2024; 3 R Todd (63319_CR6) 2014; 31 W Zhu (63319_CR24) 2019; 46.2 N Siddique (63319_CR33) 2021; 9 63319_CR20 T Shimomura (63319_CR23) 2023; 113 Z Zou (63319_CR14) 2017; 8.4 63319_CR7 K-W Li (63319_CR27) 2021; 89 EV Chalmers (63319_CR3) 2016; 53 S Tian (63319_CR19) 2019; 7 E Abadi (63319_CR30) 2018; 38.6 A Dawood (63319_CR9) 2015; 219.11 63319_CR29 63319_CR28 L Lin (63319_CR10) 2019; 21.6 M Wu (63319_CR37) 2022; 67.19 |
| References_xml | – volume: 120 start-page: 103720 year: 2020 ident: CR16 article-title: Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2020.103720 – volume: 38.6 start-page: 1457 year: 2018 end-page: 1465 ident: CR30 article-title: DukeSim: A realistic, rapid, and scanner-specific simulation framework in computed tomography publication-title: IEEE Trans. Med. Imaging – volume: 31 start-page: 36 issue: 1 year: 2014 end-page: 52 ident: CR6 article-title: Dental imaging-2 D to 3 D: A historic, current, and future view of projection radiography publication-title: Endod. Top. doi: 10.1111/etp.12067 – ident: CR18 – volume: 97 start-page: 134 year: 2023 ident: CR2 article-title: Diagnostic applications of intraoral scanners: A systematic review publication-title: J. Imaging doi: 10.3390/jimaging9070134 – volume: 6 start-page: 794 issue: 10 year: 2014 ident: CR1 article-title: Recent advances in imaging technologies in dentistry publication-title: World J. Radiol. doi: 10.4329/wjr.v6.i10.794 – volume: 44.1 start-page: 20140225 year: 2015 ident: CR5 article-title: Guidelines for clinical use of CBCT: A review publication-title: Dentomaxillofac. Radiol. doi: 10.1259/dmfr.20140225 – ident: CR12 – volume: 17.24 start-page: 9428 year: 2020 ident: CR8 article-title: CBCT and intra-oral scanner: The advantages of 3D technologies in orthodontic treatment publication-title: Int. J. Environ. Res. Public Health doi: 10.3390/ijerph17249428 – volume: 13 start-page: 2096 year: 2022 ident: CR36 article-title: A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images publication-title: Nat. Commun. doi: 10.1038/s41467-022-29637-2 – volume: 44 start-page: 20150079 issue: 8 year: 2015 ident: CR13 article-title: 3D dento-maxillary osteolytic lesion and active contour segmentation pilot study in CBCT: Semi-automatic vs manual methods publication-title: Dentomaxillofac. Radiol. doi: 10.1259/dmfr.20150079 – volume: 67.15 start-page: 155008 year: 2022 ident: CR26 article-title: Virtual computed-tomography system for deep-learning-based material decomposition publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/ac7bcd – ident: CR35 – ident: CR29 – volume: 23.7 start-page: e26151 year: 2021 ident: CR25 article-title: Clinically applicable segmentation of head and neck anatomy for radiotherapy: deep learning algorithm development and validation study publication-title: J. Med. Internet Res. doi: 10.2196/26151 – volume: 21.6 start-page: 1801013 year: 2019 ident: CR10 article-title: 3D printing and digital processing techniques in dentistry: A review of literature publication-title: Adv. Eng. Mater. doi: 10.1002/adem.201801013 – volume: 89 start-page: 182 year: 2021 end-page: 192 ident: CR27 article-title: kV–kV and kV–MV DECT based estimation of proton stopping power ratio—A simulation study publication-title: Phys. Med. doi: 10.1016/j.ejmp.2021.07.038 – volume: 7 start-page: 84817 year: 2019 end-page: 84828 ident: CR19 article-title: Automatic classification and segmentation of teeth on 3D dental model using hierarchical deep learning networks publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2924262 – volume: 9 start-page: 82031 year: 2021 end-page: 82057 ident: CR33 article-title: U-net and its variants for medical image segmentation: A review of theory and applications publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3086020 – volume: 94 start-page: 254 issue: 5 year: 2011 end-page: 265 ident: CR4 article-title: Dental cone beam CT and its justified use in oral health care publication-title: J. Belgian Soc. Radiol. doi: 10.5334/jbr-btr.662 – volume: 67.19 start-page: 194002 year: 2022 ident: CR37 article-title: XCIST-an open access x-ray/CT simulation toolkit publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/ac9174 – ident: CR15 – ident: CR17 – volume: 219.11 start-page: 521 year: 2015 end-page: 529 ident: CR9 article-title: 3D printing in dentistry publication-title: Br. Dent. J. doi: 10.1038/sj.bdj.2015.914 – ident: CR11 – volume: 45 start-page: 357 issue: 4 year: 2010 end-page: 369 ident: CR22 article-title: Adult male and female reference computational phantoms (ICRP Publication 110) publication-title: Jpn. J. Health Phys. doi: 10.5453/jhps.45.357 – volume: 46.2 start-page: 576 year: 2019 end-page: 589 ident: CR24 article-title: AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy publication-title: Med. Phys doi: 10.1002/mp.13300 – ident: CR32 – volume: 113 start-page: 102648 year: 2023 ident: CR23 article-title: Virtual cone-beam computed tomography simulator with human phantom library and its application to the elemental material decomposition publication-title: Phys. Med. doi: 10.1016/j.ejmp.2023.102648 – ident: CR34 – ident: CR7 – volume: 3 start-page: 767836 year: 2024 ident: CR21 article-title: Deep learning for automatic image segmentation in dentistry and its clinical application publication-title: Front. Med. Technol. doi: 10.3389/fmedt.2021.767836 – ident: CR28 – volume: 53 start-page: 568 issue: 5 year: 2016 end-page: 577 ident: CR3 article-title: Intraoral 3D scanning or dental impressions for the assessment of dental arch relationships in cleft care: which is superior? publication-title: Cleft Palate Craniofac. J. doi: 10.1597/15-036 – volume: 1 start-page: 612 issue: 6 year: 1984 end-page: 619 ident: CR31 article-title: Practical cone-beam algorithm publication-title: JOSA A doi: 10.1364/JOSAA.1.000612 – ident: CR20 – volume: 8.4 start-page: 774 year: 2017 end-page: 783 ident: CR14 article-title: A semi-automatic segmentation for tooth on cone beam CT volume following the anatomic guidance publication-title: J. Inf. Hiding Multim. Signal Process. – ident: 63319_CR32 doi: 10.1088/0031-9155/27/3/009 – volume: 17.24 start-page: 9428 year: 2020 ident: 63319_CR8 publication-title: Int. J. Environ. Res. Public Health doi: 10.3390/ijerph17249428 – volume: 219.11 start-page: 521 year: 2015 ident: 63319_CR9 publication-title: Br. Dent. J. doi: 10.1038/sj.bdj.2015.914 – volume: 46.2 start-page: 576 year: 2019 ident: 63319_CR24 publication-title: Med. Phys doi: 10.1002/mp.13300 – volume: 89 start-page: 182 year: 2021 ident: 63319_CR27 publication-title: Phys. Med. doi: 10.1016/j.ejmp.2021.07.038 – volume: 44.1 start-page: 20140225 year: 2015 ident: 63319_CR5 publication-title: Dentomaxillofac. Radiol. doi: 10.1259/dmfr.20140225 – ident: 63319_CR17 doi: 10.1109/TPAMI.2021.3086072 – ident: 63319_CR18 doi: 10.1007/978-3-031-43898-1_67 – volume: 67.19 start-page: 194002 year: 2022 ident: 63319_CR37 publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/ac9174 – volume: 45 start-page: 357 issue: 4 year: 2010 ident: 63319_CR22 publication-title: Jpn. J. Health Phys. doi: 10.5453/jhps.45.357 – volume: 21.6 start-page: 1801013 year: 2019 ident: 63319_CR10 publication-title: Adv. Eng. Mater. doi: 10.1002/adem.201801013 – volume: 31 start-page: 36 issue: 1 year: 2014 ident: 63319_CR6 publication-title: Endod. Top. doi: 10.1111/etp.12067 – volume: 7 start-page: 84817 year: 2019 ident: 63319_CR19 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2924262 – ident: 63319_CR35 doi: 10.1109/ISBI45749.2020.9098542 – volume: 53 start-page: 568 issue: 5 year: 2016 ident: 63319_CR3 publication-title: Cleft Palate Craniofac. J. doi: 10.1597/15-036 – ident: 63319_CR7 – ident: 63319_CR15 doi: 10.1109/CVPR.2019.00653 – volume: 13 start-page: 2096 year: 2022 ident: 63319_CR36 publication-title: Nat. Commun. doi: 10.1038/s41467-022-29637-2 – ident: 63319_CR20 doi: 10.1016/j.neucom.2023.126629 – volume: 97 start-page: 134 year: 2023 ident: 63319_CR2 publication-title: J. Imaging doi: 10.3390/jimaging9070134 – volume: 23.7 start-page: e26151 year: 2021 ident: 63319_CR25 publication-title: J. Med. Internet Res. doi: 10.2196/26151 – volume: 67.15 start-page: 155008 year: 2022 ident: 63319_CR26 publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/ac7bcd – volume: 44 start-page: 20150079 issue: 8 year: 2015 ident: 63319_CR13 publication-title: Dentomaxillofac. Radiol. doi: 10.1259/dmfr.20150079 – volume: 1 start-page: 612 issue: 6 year: 1984 ident: 63319_CR31 publication-title: JOSA A doi: 10.1364/JOSAA.1.000612 – volume: 38.6 start-page: 1457 year: 2018 ident: 63319_CR30 publication-title: IEEE Trans. Med. Imaging – volume: 94 start-page: 254 issue: 5 year: 2011 ident: 63319_CR4 publication-title: J. Belgian Soc. Radiol. doi: 10.5334/jbr-btr.662 – volume: 8.4 start-page: 774 year: 2017 ident: 63319_CR14 publication-title: J. Inf. Hiding Multim. Signal Process. – ident: 63319_CR29 – volume: 113 start-page: 102648 year: 2023 ident: 63319_CR23 publication-title: Phys. Med. doi: 10.1016/j.ejmp.2023.102648 – volume: 120 start-page: 103720 year: 2020 ident: 63319_CR16 publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2020.103720 – volume: 9 start-page: 82031 year: 2021 ident: 63319_CR33 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3086020 – volume: 3 start-page: 767836 year: 2024 ident: 63319_CR21 publication-title: Front. Med. Technol. doi: 10.3389/fmedt.2021.767836 – volume: 6 start-page: 794 issue: 10 year: 2014 ident: 63319_CR1 publication-title: World J. Radiol. doi: 10.4329/wjr.v6.i10.794 – ident: 63319_CR34 – ident: 63319_CR11 – ident: 63319_CR12 doi: 10.1007/s11517-023-02889-w – ident: 63319_CR28 doi: 10.1118/1.4955438 |
| SSID | ssib045319080 ssib045319113 ssib045318930 ssib045319110 ssib045318929 ssib045318928 ssj0000529419 ssib045319075 |
| Score | 2.4311032 |
| Snippet | In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on... Abstract In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer nii |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 12630 |
| SubjectTerms | 631/57/2266 692/308 692/700/3032 Computed tomography Computer vision Cone-Beam Computed Tomography - methods Datasets Deep Learning Dentistry Dentistry - methods Humanities and Social Sciences Humans Image Processing, Computer-Assisted - methods Medicine multidisciplinary Phantoms, Imaging Q R Science Science (multidisciplinary) Segmentation Teeth Tooth - anatomy & histology Tooth - diagnostic imaging |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB2hSpW4IKB8BFpkJG4QNfFX7CNUrKpK5USl3qzEntAcmq20W6n8e2ac7NLlQ1y45JDk4LxnZ-bFmTcA77RMyhqFJakrLLVLtmx7Eq6mb1TSfU-8cKHw-Rd7eqHPLs3lvVZf_E_YZA88AXdc66g6rKpWI-X-mJx3HGOc72NrOxX57Vv56p6Ymly9pde1n6tkKuWOVxSpuJpM6tIqrty524lE2bCf4ss4DH_KNX__ZfKXfdMcjhaP4dGcR4qP0_ifwAMcn8L-1Fny-wGcLSh7FJM5LBtriNyLT9xccc_gazGMIhfocqc30Y6JTnCWGFGsl0SdWOG367koaXwGF4vPX09Oy7ltQhmtqtelkwmb3nrZp-QT6h59VLWJBH9lE6nQzqJsVTQGvW7Q6mhUtLJLjY8k3lr1HPbG5YgvQVQqplqmLsqu0RZrh6onCZQMsYsx2QLebyAMN5M7Rsi72sqFCfBAgIcMeLgr4BOjvL2Tna3zCeI7zHyHf_FdwBFxFOLAx5rknvekYS03TTFsVFTA4Ya9MC_HVSBdJL3hsvYC3m4v00Li3ZF2xOVtvkdp60hAFfBiIns7UkU6RJM4LsDtTIOdR9m9Mg5X2aybYonmz2wFfNjMmJ_j-jtWr_4HVq_hoeQJzx-N5CHs0aTDI8qh1t2bvFx-ABjfFhk priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3Nb9UwDLdgE4ILgvFV2FCQuEG113w1OSEGe5omMSHEpN2iNkm3HtY-9t6k8d9jt-mbHl-XHtIcEtuJ_bNjG-Ct5EFoJWKO6Crm0gSdVw0CV9WUIsimQb5QovCXE310Ko_P1FlyuC3Ts8rpThwu6tB78pHvo2nLraLM5A-LHzl1jaLoamqhcRe28Qo2KOfbB4cnX7-tvSwUx5KFTdkyM2H2l6ixKKuMy1wLyuC52dBIQ-F-1DNd2_7N5vzz6eRv8dNBLc0fwcNkT7KPowA8hjux24F7Y4fJnztw_zO9BqKGbk_geI4WJRsLxlKxDTb052OLC-ojfMnajoVpMqu6gANkOfrIVj2yky3j-WVKVOqewun88Punozy1Usi9FsUqNzzEstGWNyHYEGUTrReF8siSmQ6ITGsdeSW8UtHKMmrplfCa16G0HgFdJZ7BVtd38QWwmfCh4KH2vC6ljoWJokFYFBRyPPqgM3g3kdMtxooZboh0C-NG4jskvhuI724yOCCKr2dStethoL86d-nwIDzxoo6zWSUj4r8YjDVkZxjb-ErXwmewh_xyvqVvgRDQWsS1mhqpKCpelMHuxEmXjujS3QpUBm_Wv_FwUcSk6mJ_PcwRUhsEVRk8Hxm_XqlAbCIRMGdgNkRiYyubf7r2YijgjfpFkustg_eT9Nyu69-0evn_bbyCB5zEmlxEfBe2UJziHlpMq_p1Oha_AFXJEr0 priority: 102 providerName: ProQuest – databaseName: Scholars Portal Journals: Open Access dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB5VRUhcEG8CLTISNwhs_Ip9QAgQq6pSObFSb1FiT9pINFu6i9T-e2acZNHCcuCSg-1Izjw089mZ-QBeaRmVNQpzQleYaxdtXrcEXE1bqqjblvTChcInX-3RQh-fmtM9mOiORgGudkI75pNaXH1_e_3j5gM5_PuhZNy9W1EQ4kIxqXOruCiHcspbFJksg7GTMd0fen1Lrws_1s7sfnUrPqU2_hR1-q7blYH-_SPlH7epKUjN78HdMbsUHwdzuA972D-A2wPf5M1DOJ5TTimGlrHcbkMkhj5xec5Mwhei60Uq22X-N1H3kQY4dwwo1ktSqFjh2cVYqtQ_gsX8y7fPR_lIppAHq4p17mTEsrVetjH6iLpFH1RhAillZiNh08airFUwBr0u0epgVLCyiaUPBOlq9Rj2-2WPT0HMVIiFjE2QTaktFg5VS8AoGtI5hmgzeD2JsLocemZU6a5buWoQeEUCr5LAq-sMPrGUNyu533UaWF6dVaP7EEAJqsHZrNZICBCj844zDefbUNtGhQwOSUdV6PhZEAj0npCtZSoVw-2LMjiYtFdNNlYRWpLecLF7Bi830-RefGdS97j8mdYobR3BqgyeDMre7FQROtEEmTNwW2aw9SnbM313nlp4U4TRfPiWwZvJYn7v69-yevZ_y5_DHcmmzYdG8gD2ybzwkHKodfMiOcYvizsVfg priority: 102 providerName: Scholars Portal |
| Title | Fine structural human phantom in dentistry and instance tooth segmentation |
| URI | https://cir.nii.ac.jp/crid/1871992116467451008 https://link.springer.com/article/10.1038/s41598-024-63319-x https://www.ncbi.nlm.nih.gov/pubmed/38824210 https://www.proquest.com/docview/3062958180 https://www.proquest.com/docview/3063468675 https://pubmed.ncbi.nlm.nih.gov/PMC11144222 https://doaj.org/article/14c3be00a4e144ed898818589fca6b3c |
| Volume | 14 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3da9swED-6lMFexr7rrQ0a7G0zsyVZlh7T0FACLWNbIW_Cls6tH-qUJYPtv99JtjOydQ97kUGSQb6TfPe7090BvJPcC1UITAldYSq1V2nVEHAtmlJ42TTElxAofHGpzq_kclWsDoCPsTDx0n5MaRl_0-PtsI8bEjQhGIzLVIkQeEN646Em8ccncDibLb8sd5aV4LuSuRkiZDKh73l5TwrFZP0kW7q2vU_P_Pu65B8-0yiKFk_g8aBDslm_6qdwgN0zeNhXlfz5HJYL0hxZnxg2JNVgsQ4fu7sJ9YJvWduxGJwbqryxqvPUETREh2y7JraxDV7fDgFJ3Qu4Wpx9nZ-nQ8mE1CmRb1PNPZaNMrzx3niUDRon8sIR6TPlCYHWCnklXFGgkSUq6QrhFK99aRwBt0q8hEm37vAIWCacz7mvHa9LqTDXKBqCP74gzqLzKoH3IwntXZ8Zw0aPttC2J7glgttIcPsjgdNA5d3MkNU6dqy_XduBywRDnKgxyyqJhPPQa6ODPqFN4ypVC5fACfHIuja0OUE9Ywi_qlAwpQhJihI4Hrlnh6O4sYSJuClCSHsCb3fDdIiCZ6TqcP09zhFSaQJPCbzqmb1bqSAMIgkYJ6D3tsHep-yPdO1NTNRNckQGE1sCH8Yd83td_6bV6_-b_gYe8bC1g2mIH8OEtheekKa0rafwoFyV0-GA0PP07PLTZ-qdq_k0Wh-ovZD6F3KEEmE |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VrVC5ICivQAtGghNETWzHGx8QorSr7WuFUCv1ZhLbaXNosrCLaP8Uv5GZPLZaXrde9pBYK2dm7JnP45kP4JXkTqhE-BDRlQ9l6lSYFQhck2IonCwK1AsVCh9N1PhE7p8mpyvws6-FoWuV_Z7YbNSutnRGvoWhLdcJVSa_n34NiTWKsqs9hUZrFgf-6gdCttm7vR3U72vOR7vHH8dhxyoQWiXieZhy54eF0rxwTjsvC6-tiBOLs4uUQ5CWK88zYZPEazn0StpEWMVzN9QWsU0m8H9vwaqkitYBrG7vTj59XpzqUN5MxrqrzolEujVDD0lVbFyGSlDF0OWSB2yIAtCvVWX5txj3z6uav-VrGzc4ugd3u_iVfWgN7j6s-GodbreMllfrsLZDt4-IQO4B7I8wgmVtg1pq7sEaPkA2PSfe4gtWVsz1g1lWOXxAkar1bF6j-bCZP7voCqOqh3ByI0J-BIOqrvwTYJGwLuYutzwfSuXj1IsCYZhL0MK8dSqAN704zbTt0GGazLpITSt8g8I3jfDNZQDbJPHFSOqu3Tyov52ZbrEiHLIi91GUSY9407tUpxTXpLqwmcqFDWAT9WVsSb8xQk6tEUcrIm5JqFlSABu9Jk23JczMtQEH8HLxGhczZWiyytffmzFCqhRBXACPW8UvZioQC0kE6AGkSyax9CnLb6ryvGkYjv5M0lFfAG9767me179l9fT_n_EC1sbHR4fmcG9y8AzucDJxOp7iGzBA0_KbGK3N8-fdEmHw5aZX5S8_kFB2 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VrXhcEJRHAy0YCU4QNbEdJz4gRNmu-oBVhajUm0lsp82h2S27iPav8euYyWOr5XXrJQfHipx5eObzeGYAXkruhEqEDxFd-VBmToV5icA1KVPhZFkiXyhR-NNY7R7J_ePkeAV-9rkwdK2y3xObjdpNLJ2Rb6Fry3XSZCaX3bWIw-Ho3fQ8pA5SFGnt22m0InLgL38gfJu93Rsir19xPtr58mE37DoMhFaJeB5m3Pm0VJqXzmnnZem1FXFicaWRcgjYCuV5LmySeC1Tr6RNhFW8cKm2iHNygd-9AaspWkU5gNXtnfHh58UJD8XQZKy7TJ1IZFsztJaU0cZlqARlD10sWcOmaQDauLqq_ubv_nlt87fYbWMSR_fgbufLsvet8N2HFV-vwc22u-XlGtwe0k0kaib3APZH6M2ytlgtFfpgTW9ANj2lHsZnrKqZ6yezvHY4QF6r9Ww-QVFiM39y1iVJ1Q_h6FqI_AgG9aT268AiYV3MXWF5kUrl48yLEiGZS1DavHUqgNc9Oc20rdZhmii7yExLfIPENw3xzUUA20TxxUyqtN0MTL6dmE5xERpZUfgoyqVH7OldpjPycTJd2lwVwgawifwytqJnjPBTa8TUipq4JFQ4KYCNnpOm2x5m5kqYA3ixeI2KTdGavPaT780cIVWGgC6Axy3jFysViIskgvUAsiWRWPqV5Td1ddoUD0fbJunYL4A3vfRcrevftHry_994DrdQG83HvfHBU7jDScLppIpvwAAly2-i4zYvnnUawuDrdSvlL91fVKI |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Fine+structural+human+phantom+in+dentistry+and+instance+tooth+segmentation&rft.jtitle=Scientific+reports&rft.au=Takeya%2C+Atsushi&rft.au=Watanabe%2C+Keiichiro&rft.au=Haga%2C+Akihiro&rft.date=2024-06-02&rft.pub=Nature+Publishing+Group+UK&rft.eissn=2045-2322&rft.volume=14&rft.issue=1&rft_id=info:doi/10.1038%2Fs41598-024-63319-x&rft.externalDocID=10_1038_s41598_024_63319_x |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon |