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Dental root canal segmentation from super-resolved 3D cone beam computed tomography data

Abstract : This paper aims at evaluating the potential of super-resolution (SR) image processing to enhance the resolution of Cone Beam Computed Tomography (CBCT) images and to further improve the root canal segmentation in endodontics. First we perform SR based on a linear model, then, we apply an automated segmentation procedure to native and super-resolved CBCT volumes in order to extract the root canal structure. Seven intact extracted teeth have been used to evaluate the potential of SR CBCT in detecting the dental root canal. For all the considered teeth, the SR CBCT volumes provided a smaller error compared to the native CBCT data.
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Submitted on : Monday, April 20, 2020 - 2:31:17 PM
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  • HAL Id : hal-02548035, version 1
  • OATAO : 24693

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Rose Sfeir, Jérôme Michetti, Bilal Chebaro, Franck Diemer, Adrian Basarab, et al.. Dental root canal segmentation from super-resolved 3D cone beam computed tomography data. IEEE Nuclear Science Symposium and Medical Imaging Conference (2017), Oct 2017, Atlanta, United States. pp.1-2. ⟨hal-02548035⟩

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