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Deep Learning-Based Super-Resolution Applied to Dental Computed Tomography

Hatvani, Janka and Horvath, Andras and Michetti, Jérôme and Basarab, Adrian and Kouamé, Denis and Gyöngy, Miklos Deep Learning-Based Super-Resolution Applied to Dental Computed Tomography. (2019) IEEE Transactions on Radiation and Plasma Medical Sciences, 3 (2). 120-128. ISSN 2469-7311

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Official URL: https://doi.org/10.1109/TRPMS.2018.2827239


The resolution of dental computed tomography (CT) images is limited by detector geometry, sensitivity, patient movement, the reconstruction technique and the need to minimize radiation dose. Recently, the use of convolutional neural network (CNN) architectures has shown promise as a resolution enhancement method. In the current work, two CNN architectures—a subpixel network and the so called U-net—have been considered for the resolution enhancement of 2-D cone-beam CT image slices of ex vivo teeth. To do so, a training set of 5680 cross-sectional slices of 13 teeth and a test set of 1824 slices of 4 structurally different teeth were used. Two existing reconstruction-based super-resolution methods using l2-norm and total variation regularization were used for comparison. The results were evaluated with different metrics (peak signal-to-noise ratio, structure similarity index, and other objective measures estimating human perception) and subsequent image-segmentation-based analysis. In the evaluation, micro-CT images were used as ground truth.The results suggest the superiority of the proposed CNN-based approaches over reconstruction-based methods in the case of dental CT images, allowing better detection of medically salient features, such as the size, shape, or curvature of the root canal.

Item Type:Article
Additional Information:Machine learning in radiation based medical sciences
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Pázmány Péter Catholic University - PPCU (HUNGARY)
Laboratory name:
Deposited On:12 Mar 2020 15:10

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