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Automatic teeth segmentation on panoramic X-rays using deep neural networks

Abstract : In order to build an intelligent dental care process that both facilitates the treatment and improves the diagnosis, an accurate tooth segmentation and recognition on panoramic X-ray images might prove helpful. Although many studies have been conducted on teeth segmentation, few methods allow to perform tooth recognition and numbering at the same time. The existing methods allowing both those processes rely on instance segmentation architectures. To fill some gaps in the area of dental image segmentation, we propose a novel approach of automatic joint teeth segmentation and numbering using the pioneer U-Net model. We are first to employ the conventional U-Net model and show its limitations to provide accurate segmentation, being affected by noisy pixels outside the teeth region and by missing teeth in the X-ray images. To overcome this problem and reduce the misclassifications, we use a bounding box prior at the level of the skip connections. Such an approach helps guiding the network to better locate the teeth, and hence improves the segmentation. To validate the effectiveness of the method, we have conducted two experiments on the DNS Panoramic Dataset: a first one using manual bounding boxes and another one relying on a preliminary step of object detection. The implemented networks were evaluated using the Dice coefficient index and our results showed that consideration of location information onto the skip connections improves the performances of the semantic segmentation by 5% to 10% in average Dice accuracy depending on the quality of the bounding box labels.
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Contributor : Florent Autrusseau Connect in order to contact the contributor
Submitted on : Wednesday, May 18, 2022 - 3:01:32 PM
Last modification on : Saturday, June 25, 2022 - 3:53:06 AM


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  • HAL Id : hal-03671003, version 1



Rafic Nader, Andrey Smorodin, Natalia de La Fourniere, yves Amouriq, Florent Autrusseau. Automatic teeth segmentation on panoramic X-rays using deep neural networks. 2022. ⟨hal-03671003⟩



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