Authors:	Rahbani, Dana Fliss, Barbara Ebert, Lars Christian Bjelopavlovic, Monika
Title:	Detecting missing teeth on PMCT using statistical shape modeling
Online publication date:	4-May-2023
Year of first publication:	2023
Language:	english
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.
DDC:	610 Medizin 610 Medical sciences
Institution:	Johannes Gutenberg-Universität Mainz
Department:	FB 04 Medizin
Place:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-9044
Version:	Published version
Publication type:	Zeitschriftenaufsatz
License:	CC BY
Information on rights of use:	https://creativecommons.org/licenses/by/4.0/
Journal:	Forensic science, medicine, and pathology Version of Record (VoR)
Publisher:	Springer
Issue date:	2023
ISSN:	1556-2891
Publisher DOI:	10.1007/s12024-023-00590-w
Appears in collections:	DFG-491381577-H