Gutenberg Open Science: Detecting missing teeth on PMCT using statistical shape modeling

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9044

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DC Field	Value	Language
dc.contributor.author	Rahbani, Dana	-
dc.contributor.author	Fliss, Barbara	-
dc.contributor.author	Ebert, Lars Christian	-
dc.contributor.author	Bjelopavlovic, Monika	-
dc.date.accessioned	2023-05-04T09:38:16Z	-
dc.date.available	2023-05-04T09:38:16Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/9061	-
dc.description.abstract	The identification of teeth in 3D medical images can be a first step for victim identification from scant remains, for comparison of ante- and postmortem images or for other forensic investigations. We evaluate the performance of a tooth detection approach on mandibles with missing parts or pathologies based on statistical shape models. The proposed approach relies on a shape model that has been built from the full lower jaw, including the mandible and teeth. The model is fitted to the target, resulting in a reconstruction, in addition to a label map that indicates the presence or absence of teeth. We evaluate the accuracy of the proposed solution on a dataset consisting of 76 target mandibles, all extracted from CT images and exhibiting various cases of missing teeth or other cases, such as roots, implants, first dentition, and gap closure. We show an accuracy of approximately 90% on the front teeth (including incisors and canines in our study) that decreases for the molars due to high false-positive rates at the wisdom teeth level. Despite the drop in performance, the proposed approach can be used to obtain an estimate of the tooth count without wisdom teeth, tooth identification, reconstruction of the existing teeth to automate measurements taken as part of routine forensic procedures, or prediction of the missing teeth shape. In comparison to other approaches, our solution relies solely on shape information. This means it can be applied to cases obtained from either medical images or 3D scans because it does not depend on the imaging modality intensities. Another novelty is that the proposed solution avoids heuristics for the separation of teeth or for fitting individual tooth models. The solution is therefore not target-specific and can be directly applied to detect missing parts in other target organs using a shape model of the new target.	en_GB
dc.description.sponsorship	Deutsche Forschungsgemeinschaft (DFG)\|491381577\|Open-Access-Publikationskosten 2022–2024 Universität Mainz - Universitätsmedizin	-
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.title	Detecting missing teeth on PMCT using statistical shape modeling	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-9044	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 04 Medizin	de
jgu.organisation.number	2700	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Forensic science, medicine, and pathology	de
jgu.journal.volume	Version of Record (VoR)	de
jgu.publisher.year	2023	-
jgu.publisher.name	Springer	de
jgu.publisher.issn	1556-2891	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	610	de
jgu.publisher.doi	10.1007/s12024-023-00590-w	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Lebenswissenschaften	de
Appears in collections:	DFG-491381577-H

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