@InProceedings{ Sf2017.1,
author = {Sfeir, Rose and Michetti, Jérôme and Chebaro, Bilal and Diemer, Franck and Basarab, Adrian and Kouamé, Denis},
title = "{Dental root canal segmentation from super-resolved 3D cone beam computed tomography data }",
booktitle = "{IEEE Nuclear Science Symposium and Medical Imaging Conference, Atlanta, 21/10/2017-28/10/2017}",
year = {2017},
month = {octobre},
publisher = {IEEE : Institute of Electrical and Electronics Engineers},
address = {http://www.ieee.org/},
pages = {1--2},
language = {anglais},
URL = {https://doi.org/10.1109/NSSMIC.2017.8533054 - https://oatao.univ-toulouse.fr/24693/},
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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