Automated Tooth Instance Segmentation and Pathology Annotation Pipeline for Panoramic Radiographs

Caries detection in dental radiographs is a challenging and time consuming task even for experts in the field. Recent studies have shown the potential of tooth instance segmentation and caries detection with neural networks. We present a tooth level pathology annotation pipeline, based on automated tooth instance segmentation and numbering with a Mask-R-CNN architecture followed by the extraction of the bounding boxes of individual teeth as patches, that can be reassembled to the original image. 5-fold cross validation resulted in mean average precision (mAP) of 0.898 ± 0.02 for tooth instance segmentation. Augmentation focusing on elastic transformation increased the mAP by 0.053 to 0.951 ± 0.014 and enhanced robustness across folds. At performance levels at least similar to published data our approach provides flexibility for patch-based pathology diagnosis combined with the option to reassemble annotated patches to the original image. This will permit combining tooth-number-specific, neighborhood-based and entire image based features in future modeling along with tooth-centric review and diagnoses by clinical needs of dentists.