Numbering permanent and deciduous teeth via deep instance segmentation in panoramic x-rays

Abstract

Panoramic X-rays are an essential tool to assist dentistry experts in their diagnostic procedures. Dentists can analyze the anatomical and pathological structures while planing orthodontic, periodontal, and surgical treatments. Even though detecting, numbering, and segmenting teeth are essential tasks to leverage automatic analysis on panoramic X-rays, it is lacking in the literature a study and a data set that considers at the same time deciduous and permanent teeth in a wide variety of panoramic X-rays. To fill this gap, this work introduces a novel, challenging, and high-variable public data set labeled from scratch. This data set incorporates new elements such as instance overlapping and deciduous teeth, supporting our study on tooth numbering and segmentation. Our efforts aim to improve the segmentation on the boundaries because they are the main hurdle of the instance segmentation methods. For that, we investigate and compare (quantitatively and qualitatively) two Mask R-CNN-based solutions: the standard one, with a fully convolutional network, and another one that employs the PointRend module on the top. Our findings attest to the feasibility of extending segmentation and numbering to deciduous teeth through end-to-end deep learning architectures, as well as, the higher performance of the Mask R-CNN with PointRend either on instance segmentation (mAP of +2 percentage points) or the numbering (mAP of +1.2 percentage points) on the test data set. We hope that our findings and our new data set support the development of new tools to assist professionals in faster diagnosis, making upon panoramic X-rays.