Abstract
A very accurate detection is required for fitting 3D dental model onto color images for tracking the milimetric displacement of each tooth along orthodontics treatment. Detecting the teeth boundaries with high accuracy on these images is a challenging task because of the various quality and high resolution of images. By training Mask R-CNN on a very large dataset of 170k images of patients’ mouth taken with different mobile devices, we have a reliable teeth instance segmentation, but each tooth boundaries are not accurate enough for dental care monitoring. To address this problem, we propose an efficient method for object contour refinement using instance segmentation (CRIS). Instance segmentation provides high-level information on the location and the shape of the object to guide and refine locally the contour detection process. We evaluate CRIS method on a large dataset of 600 dental images. Our method improves significantly the efficiency of several state-of-the-art contour detectors: Canny (+32.0% in ODS F-score), gPb (+17.8%), Sketch Tokens (+17.3%), Structured Edge (+12.2%), DeepContour (+15.5%), HED (+2.9%), CEDN (+2.2%), RCF (+2.2%) and also the best result (ODS F-score of 0.819). Our CRIS method can be used with any contour detection algorithms to refine object contours. In that way, this approach is promising for other applications requiring very accurate contour detection.
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Van Pham, T., Lucas, Y., Treuillet, S., Debraux, L. (2020). Object Contour Refinement Using Instance Segmentation in Dental Images. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_9
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