Literature DB >> 24078371

Automated implant segmentation in cone-beam CT using edge detection and particle counting.

Ruben Pauwels, Reinhilde Jacobs, Hilde Bosmans, Pisha Pittayapat, Pasupen Kosalagood, Onanong Silkosessak, Soontra Panmekiate.   

Abstract

PURPOSE: To develop a fully automated, accurate and robust segmentation technique for dental implants on cone-beam CT (CBCT) images.
METHODS: A head-size cylindrical polymethyl methacrylate phantom was used, containing titanium rods of 5.15 mm diameter. The phantom was scanned on 17 CBCT devices, using a total of 39 exposure protocols. Images were manually thresholded to verify the applicability of adaptive thresholding and to determine a minimum threshold value (Tmin). A three-step automatic segmentation technique was developed. Firstly, images were pre-thresholded using Tmin. Next, edge enhancement was performed by filtering the image with a Sobel operator. The filtered image was thresholded using an iteratively determined fixed threshold (Tedge) and converted to binary. Finally, a particle counting method was used to delineate the rods. The segmented area of the titanium rods was compared to the actual area, which was corrected for phantom tilting.
RESULTS: Manual thresholding resulted in large variation in threshold values between CBCTs. After applying the edgeenhancing filter, a stable Tedge value of 7.5% was found. Particle counting successfully detected the rods for all but one device. Deviations between the segmented and real area ranged between -2.7 and +14.4mm(2) with an average absolute error of 2.8mm(2). Considering the diameter of the segmented area, submillimeter accuracy was seen for all but two data sets.
CONCLUSION: A segmentation technique was defined which can be applied to CBCT data for an accurate and fully automatic delineation of titanium rods. The technique was validated in vitro and will be further tested and refined on patient data.

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Year:  2014        PMID: 24078371     DOI: 10.1007/s11548-013-0946-z

Source DB:  PubMed          Journal:  Int J Comput Assist Radiol Surg        ISSN: 1861-6410            Impact factor:   2.924


  20 in total

1.  Quantification of metal artifacts on cone beam computed tomography images.

Authors:  Ruben Pauwels; Harry Stamatakis; Hilde Bosmans; Ria Bogaerts; Reinhilde Jacobs; Keith Horner; Kostas Tsiklakis
Journal:  Clin Oral Implants Res       Date:  2011-12-15       Impact factor: 5.977

2.  An in vitro comparison of subjective image quality of panoramic views acquired via 2D or 3D imaging.

Authors:  P Pittayapat; D Galiti; Y Huang; K Dreesen; M Schreurs; P Couto Souza; I R F Rubira-Bullen; F H Westphalen; R Pauwels; G Kalema; G Willems; R Jacobs
Journal:  Clin Oral Investig       Date:  2012-03-01       Impact factor: 3.573

Review 3.  Maxillofacial cone beam computed tomography: essence, elements and steps to interpretation.

Authors:  William C Scarfe; Z Li; W Aboelmaaty; S A Scott; A G Farman
Journal:  Aust Dent J       Date:  2012-03       Impact factor: 2.291

4.  Assessment of bone segmentation quality of cone-beam CT versus multislice spiral CT: a pilot study.

Authors:  Miet Loubele; Frederik Maes; Filip Schutyser; Guy Marchal; Reinhilde Jacobs; Paul Suetens
Journal:  Oral Surg Oral Med Oral Pathol Oral Radiol Endod       Date:  2006-04-21

Review 5.  Artefacts in CBCT: a review.

Authors:  R Schulze; U Heil; D Gross; D D Bruellmann; E Dranischnikow; U Schwanecke; E Schoemer
Journal:  Dentomaxillofac Radiol       Date:  2011-07       Impact factor: 2.419

6.  Calibrated segmentation of CBCT and CT images for digitization of dental prostheses.

Authors:  Veerle Wouters; Wouter Mollemans; Filip Schutyser
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-05-03       Impact factor: 2.924

7.  Variability of dental cone beam CT grey values for density estimations.

Authors:  R Pauwels; O Nackaerts; N Bellaiche; H Stamatakis; K Tsiklakis; A Walker; H Bosmans; R Bogaerts; R Jacobs; K Horner
Journal:  Br J Radiol       Date:  2013-01       Impact factor: 3.039

8.  Implant diameter and length influence on survival: interim results during the first 2 years of function of implants by a single manufacturer.

Authors:  Eitan Mijiritsky; Ziv Mazor; Adi Lorean; Liran Levin
Journal:  Implant Dent       Date:  2013-08       Impact factor: 2.454

9.  The validity of in vivo tooth volume determinations from cone-beam computed tomography.

Authors:  Yi Liu; Raphael Olszewski; Emanuel Stefan Alexandroni; Reyes Enciso; Tianmin Xu; James K Mah
Journal:  Angle Orthod       Date:  2010-01       Impact factor: 2.079

10.  Development and applicability of a quality control phantom for dental cone-beam CT.

Authors:  Ruben Pauwels; Harry Stamatakis; Giorgos Manousaridis; Adrian Walker; Koen Michielsen; Hilde Bosmans; Ria Bogaerts; Reinhilde Jacobs; Keith Horner; Kostas Tsiklakis
Journal:  J Appl Clin Med Phys       Date:  2011-11-15       Impact factor: 2.102

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  6 in total

1.  Intraoperative detection and localization of cylindrical implants in cone-beam CT image data.

Authors:  Joseph Görres; Michael Brehler; Jochen Franke; Karl Barth; Sven Y Vetter; Andrés Córdova; Paul A Grützner; Hans-Peter Meinzer; Ivo Wolf; Diana Nabers
Journal:  Int J Comput Assist Radiol Surg       Date:  2014-04-18       Impact factor: 2.924

2.  Known-component metal artifact reduction (KC-MAR) for cone-beam CT.

Authors:  A Uneri; X Zhang; T Yi; J W Stayman; P A Helm; G M Osgood; N Theodore; J H Siewerdsen
Journal:  Phys Med Biol       Date:  2019-08-21       Impact factor: 3.609

3.  Spinal pedicle screw planning using deformable atlas registration.

Authors:  J Goerres; A Uneri; T De Silva; M Ketcha; S Reaungamornrat; M Jacobson; S Vogt; G Kleinszig; G Osgood; J-P Wolinsky; J H Siewerdsen
Journal:  Phys Med Biol       Date:  2017-02-08       Impact factor: 4.174

4.  C-arm orbits for metal artifact avoidance (MAA) in cone-beam CT.

Authors:  P Wu; N Sheth; A Sisniega; A Uneri; R Han; R Vijayan; P Vagdargi; B Kreher; H Kunze; G Kleinszig; S Vogt; S F Lo; N Theodore; J H Siewerdsen
Journal:  Phys Med Biol       Date:  2020-08-19       Impact factor: 4.174

5.  Deep learning-based dental implant recognition using synthetic X-ray images.

Authors:  Aviwe Kohlakala; Johannes Coetzer; Jeroen Bertels; Dirk Vandermeulen
Journal:  Med Biol Eng Comput       Date:  2022-08-18       Impact factor: 3.079

6.  Impact of the blooming artefact on dental implant dimensions in 13 cone-beam computed tomography devices.

Authors:  Victor Aquino Wanderley; Karla de Faria Vasconcelos; Andre Ferreira Leite; Ruben Pauwels; Sohaib Shujaat; Reinhilde Jacobs; Matheus L Oliveira
Journal:  Int J Implant Dent       Date:  2021-07-14
  6 in total

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