Assessment of automatic segmentation of teeth using a watershed-based method.

OBJECTIVE Tooth 3D automatic segmentation (AS) is being actively developed in research and clinical fields. Here, we assess the effect of automatic segmentation using a watershed-based method on the accuracy and reproducibility of 3D reconstructions in volumetric measurements by comparing it with a semi-automatic segmentation(SAS) method that has already been validated. METHODS The study sample comprised 52 teeth, scanned with micro-CT (41 µm voxel size) and CBCT (76; 200 and 300 µm voxel size). Each tooth was segmented by AS based on a watershed method and by SAS. For all surface reconstructions, volumetric measurements were obtained and analysed statistically. Surfaces were then aligned using the SAS surfaces as the reference. The topography of the geometric discrepancies was displayed by using a colour map allowing the maximum differences to be located. RESULTS AS reconstructions showed similar tooth volumes when compared with SAS for the 41 µm voxel size. A difference in volumes was observed, and increased with the voxel size for CBCT data. The maximum differences were mainly found at the cervical margins and incisal edges but the general form was preserved. CONCLUSION Micro-CT, a modality used in dental research, provides data that can be segmented automatically, which is timesaving. AS with CBCT data enables the general form of the region of interest to be displayed. However, our AS method can still be used for metrically reliable measurements in the field of clinical dentistry if some manual refinements are applied.

[1]  Fernand Meyer The watershed concept and its use in segmentation : a brief history , 2012, ArXiv.

[2]  A Gahleitner,et al.  Cone beam CT: a current overview of devices. , 2013, Dento maxillo facial radiology.

[3]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[4]  Richard Beare,et al.  Marker-based watershed transform method for fully automatic mandibular segmentation from CBCT images. , 2019, Dento maxillo facial radiology.

[5]  S S Naumovich,et al.  Three-dimensional reconstruction of teeth and jaws based on segmentation of CT images using watershed transformation. , 2015, Dento maxillo facial radiology.

[6]  Jean Dumoncel,et al.  Comparison of the accuracy of 3-dimensional cone-beam computed tomography and micro-computed tomography reconstructions by using different voxel sizes. , 2014, Journal of endodontics.

[7]  John P. A. Ioannidis,et al.  How to Make More Published Research True , 2014, PLoS medicine.

[8]  Sang J. Lee,et al.  Guided Immediate Implant Placement with Wound Closure by Computer-Aided Design/Computer-Assisted Manufacture Sealing Socket Abutment: Case Report. , 2017, The International journal of oral & maxillofacial implants.

[9]  M Hashimoto,et al.  Age estimation based on three-dimensional measurement of mandibular central incisors in Japanese. , 2009, Forensic science international.

[10]  Jean-Jacques Hublin,et al.  Technical note: compatibility of microtomographic imaging systems for dental measurements. , 2007, American journal of physical anthropology.

[11]  R Yanagisawa,et al.  Tooth shape reconstruction from dental CT images with the region-growing method. , 2014, Dento maxillo facial radiology.

[12]  B. Hassan,et al.  Feasibility of Cone‐beam Computed Tomography in Detecting Lateral Canals before and after Root Canal Treatment: An Ex Vivo Study , 2017, Journal of endodontics.

[13]  Robert J. Lee,et al.  Three-dimensional monitoring of root movement during orthodontic treatment. , 2015, American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics.

[14]  G. Subsol,et al.  The enamel–dentine junction in the postcanine dentition of Australopithecus africanus: intra‐individual metameric and antimeric variation , 2010, Journal of anatomy.

[15]  A. Kato,et al.  Construction of three-dimensional tooth model by micro-computed tomography and application for data sharing , 2009, Clinical Oral Investigations.

[16]  K. Kula,et al.  Estimating the location of the center of resistance of canines. , 2016, The Angle orthodontist.

[17]  L. Jahangiri,et al.  Scan‐layered reconstructions: A pilot study of a nondestructive dental histoanatomical analysis method and digital workflow to create restorations driven by natural dentin and enamel morphology , 2017, Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.].

[18]  José Braga,et al.  Dental developmental pattern of the Neanderthal child from Roc de Marsal: a high-resolution 3D analysis. , 2009, Journal of human evolution.

[19]  Sim Heng Ong,et al.  A level-set based approach for anterior teeth segmentation in cone beam computed tomography images , 2014, Comput. Biol. Medicine.

[20]  E. Kruger,et al.  Age estimation in adults by dental imaging assessment systematic review. , 2017, Forensic science international.

[21]  Stefan Zachow,et al.  Automatic Detection and Classification of Teeth in CT Data , 2012, MICCAI.

[22]  Bin Zhang,et al.  A micro-CT study of microstructure change of alveolar bone during orthodontic tooth movement under different force magnitudes in rats , 2017, Experimental and therapeutic medicine.

[23]  Marc Secanell,et al.  Comparison of in vivo 3D cone-beam computed tomography tooth volume measurement protocols , 2014, Progress in Orthodontics.

[24]  Nils-Claudius Gellrich,et al.  Advances in assessing the volume of odontogenic cysts and tumors in the mandible: a retrospective clinical trial , 2013, Head & Face Medicine.

[25]  D. Pei,et al.  Generation of tooth–periodontium complex structures using high-odontogenic potential dental epithelium derived from mouse embryonic stem cells , 2017, Stem Cell Research & Therapy.

[26]  U. Braegger,et al.  A digital approach for one-step formation of the supra-implant emergence profile with an individualized CAD/CAM healing abutment. , 2016, Journal of prosthodontic research.

[27]  Yasufumi Yamanishi,et al.  In vitro fatigue tests and in silico finite element analysis of dental implants with different fixture/abutment joint types using computer-aided design models. , 2018, Journal of prosthodontic research.

[28]  J. L. Kahn,et al.  Accuracy of 3D Reconstructions Based on Cone Beam Computed Tomography , 2010, Journal of dental research.

[29]  Jérôme Michetti,et al.  Validation of cone beam computed tomography as a tool to explore root canal anatomy. , 2010, Journal of endodontics.

[30]  P. Rüegsegger,et al.  Three-dimensional Analysis of Root Canal Geometry by High-resolution Computed Tomography , 2000, Journal of dental research.

[31]  D. Halazonetis,et al.  Estimation of root inclination of anterior teeth from virtual study models: accuracy of a commercial software , 2019, Progress in orthodontics.

[32]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Jyh-Cheng Chen,et al.  An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images , 2016, PloS one.

[34]  L. Ferrante,et al.  Statistical methods to assess the reliability of measurements in the procedures for forensic age estimation , 2009, International Journal of Legal Medicine.

[35]  Tianmin Xu,et al.  The validity of in vivo tooth volume determinations from cone-beam computed tomography. , 2010, The Angle orthodontist.

[36]  Xiaojing Liu,et al.  Root Canal Anatomy of Maxillary First Premolar by Microscopic Computed Tomography in a Chinese Adolescent Subpopulation , 2019, BioMed research international.