A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films.

ABSTRACT: We propose using faster regions with convolutional neural network features (faster R-CNN) in the TensorFlow tool package to detect and number teeth in dental periapical films. To improve detection precisions, we propose three post-processing techniques to supplement the baseline faster R-CNN according to certain prior domain knowledge. First, a filtering algorithm is constructed to delete overlapping boxes detected by faster R-CNN associated with the same tooth. Next, a neural network model is implemented to detect missing teeth. Finally, a rule-base module based on a teeth numbering system is proposed to match labels of detected teeth boxes to modify detected results that violate certain intuitive rules. The intersection-over-union (IOU) value between detected and ground truth boxes are c

SUBMITTER: Chen H 

PROVIDER: S-EPMC6405755 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature