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

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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 \(({T}_{\mathrm{min}})\). A three-step automatic segmentation technique was developed. Firstly, images were pre-thresholded using \({T}_{\mathrm{min}}\). Next, edge enhancement was performed by filtering the image with a Sobel operator. The filtered image was thresholded using an iteratively determined fixed threshold \(({T}_{\mathrm{edge}})\) 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 edge-enhancing filter, a stable \({T}_{\mathrm{edge}}\) 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 +\(14.4\,\hbox {mm}^{2}\) with an average absolute error of \(2.8\,\hbox {mm}^{2}\). 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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Acknowledgments

This study was supported by the Exchange Collaborative Research Grant of the Faculty of Dentistry, Chulalongkorn University, and the Research Fellowship Grant of the European Academy of Dentomaxillofacial Radiology (EADMFR).

Conflict of interest

Ruben Pauwels, Reinhilde Jacobs, Hilde Bosmans, Pisha Pittayapat, Pasupen Kosalagood, Onanong Silkosessak and Soontra Panmekiate declare that they have no conflict of interest.

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Authors and Affiliations

  1. Department of Radiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand

    Ruben Pauwels, Pisha Pittayapat, Pasupen Kosalagood, Onanong Silkosessak & Soontra Panmekiate

  2. Oral Imaging Center, OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, Catholic University of Leuven, Leuven, Belgium

    Ruben Pauwels, Reinhilde Jacobs & Pisha Pittayapat

  3. Dentistry, University Hospitals Leuven, Leuven, Belgium

    Reinhilde Jacobs

  4. Radiology, Department of Imaging and Pathology, Faculty of Medicine, Catholic University of Leuven, Leuven, Belgium

    Hilde Bosmans

Authors
  1. Ruben Pauwels
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  2. Reinhilde Jacobs
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  3. Hilde Bosmans
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  4. Pisha Pittayapat
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  5. Pasupen Kosalagood
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  6. Onanong Silkosessak
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  7. Soontra Panmekiate
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Correspondence to Ruben Pauwels.

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Pauwels, R., Jacobs, R., Bosmans, H. et al. Automated implant segmentation in cone-beam CT using edge detection and particle counting. Int J CARS 9, 733–743 (2014). https://doi.org/10.1007/s11548-013-0946-z

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  • DOI: https://doi.org/10.1007/s11548-013-0946-z

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