Recognition and segmentation of teeth and mandibular nerve canals in panoramic dental X-rays by Mask RCNN

Abstract

This study proposes a method based on the instance segmentation algorithm Mask RCNN for recognizing and segmenting 32 teeth and 2 mandibular nerve canals in panoramic dental X-rays. Compared with Faster RCNN, Mask RCNN has an additional semantic segmentation branch, which could directly obtain the position of teeth and the information about mandibular nerve canals in this study. At the same time, Mask RCNN introduces ROI Align, making Mask segmentation more accurate. A total of 120 training sets, 40 validation sets, and 120 test sets were used in the training and testing process. Forty of the test sets were used to test the full-tooth panoramic dental X-rays, and 40 were used for the edentulous panoramic dental X-rays, with 40 for the mandibular canals. The average precision, recall, and F1-score values of the full teeth were 96.59 %, 96.4 %, and 96.5 %, respectively. The Pm value of the defined edentulous panoramic dental X-rays was 95.41 %, and the average precision, recall, and F1-score values of mandibular canals were 76.05 %, 83.70 %, and 78.10 %, respectively. The results showed that the algorithm could effectively identify each tooth, including missing teeth and mandibular nerve canals in panoramic dental X-rays, solving the problem of too many oral structures in panoramic dental X-rays, which makes it difficult to determine the overall oral state. This study will help other researchers promote the research on artificial intelligence-assisted diagnosis in the oral field.