Tooth detection and numbering in panoramic radiographs using convolutional neural networks

Abstract

Objectives: Analysis of dental radiographs is an important part of the diagnostic process in daily clinical practice. Interpretation by an expert includes teeth detection and numbering. In this project, a novel solution based on convolutional neural networks (CNNs) is proposed that performs this task automatically for panoramic radiographs.

Methods: A data set of 1352 randomly chosen panoramic radiographs of adults was used to train the system. The CNN-based architectures for both teeth detection and numbering tasks were analyzed. The teeth detection module processes the radiograph to define the boundaries of each tooth. It is based on the state-of-the-art Faster R-CNN architecture. The teeth numbering module classifies detected teeth images according to the FDI notation. It utilizes the classical VGG-16 CNN together with the heuristic algorithm to improve results according to the rules for spatial arrangement of teeth. A separate testing set of 222 images was used to evaluate the performance of the system and to compare it to the expert level.

Results: For the teeth detection task, the system achieves the following performance metrics: a sensitivity of 0.9941 and a precision of 0.9945. For teeth numbering, its sensitivity is 0.9800 and specificity is 0.9994. Experts detect teeth with a sensitivity of 0.9980 and a precision of 0.9998. Their sensitivity for tooth numbering is 0.9893 and specificity is 0.9997. The detailed error analysis showed that the developed software system makes errors caused by similar factors as those for experts.

Conclusions: The performance of the proposed computer-aided diagnosis solution is comparable to the level of experts. Based on these findings, the method has the potential for practical application and further evaluation for automated dental radiograph analysis. Computer-aided teeth detection and numbering simplifies the process of filling out digital dental charts. Automation could help to save time and improve the completeness of electronic dental records.

Keywords: computer-aided diagnostics; convolutional neural networks; panoramic radiograph; radiographic image interpretation; teeth detection and numbering.

Figure 1.

System architecture and pipeline: the system consists of two modules for teeth detection and teeth classification. The teeth detection module finds teeth on the original panoramic radiograph outputting the bounding boxes. The teeth classification module classifies each tooth to assign a number according to the dental notation and applies a heuristic method to ensure arrangement consistency among the detected teeth. CNNs, convolutional neural networks.

Figure 2.

Teeth detection results: (a) all 32 teeth were detected, (b) severely decayed and impacted teeth were detected, (c) implants were excluded and dental crowns were detected, (d) cantilever elements of fixed bridges were excluded.

Figure 3.

Teeth detection errors produced by the system: for each case, the left image shows the boxes annotated by the experts, the right image shows the boxes detected by the system. False positives: (a) an extra box for the multiple-root tooth was detected, (b) an implant was classified as a tooth. False negatives: (c) a root remnant was missed, (d) teeth obstructed by a prosthetic construction were missed.

Figure 4.

Teeth detection errors produced by the experts: for each case, the left image shows the boxes annotated by the experts, the right image shows the boxes detected by the system. False positives: (a) persistent deciduous tooth was annotated. False negatives: (b) a whole tooth was missed, (c) a root remnant was missed, (d) a tooth obstructed by another one was missed.

Figure 5.

Teeth numbering results: (a) all 32 teeth were correctly classified, (b) severely decayed and impacted teeth were correctly classified, (c) teeth with dental crowns were correctly classified, (d) teeth were correctly classified considering the missed teeth and lack of context.

Figure 6.

Teeth numbering errors produced by the system: for each case, the classification provided by the software is at the top, the expert annotation is at the bottom. (a) decayed tooth 47 was misclassified, (b) tooth 17 (severely decayed) was misclassified, (c) teeth 13, 14 obstructed by a prosthetic device were misclassified, (d) tooth 28 was misclassified probably due to the lack of context (missing neighbouring teeth).

Figure 7.

Teeth numbering errors produced by the experts: for each case, the system classification result is at the top, the expert annotation is at the bottom. (a) teeth 26, 27 were misclassified, (b) tooth 48 was misclassified, (c) a root remnant of 28 was misclassified, (d) tooth 38 was misclassified.

Figure 8.

Sensitivity and specificity plots for all teeth numbers. The plots show sensitivity and specificity for each of eight teeth numbers averaged by four quadrants: (a1–b1) sensitivity and specificity for the system, (a2–b2) sensitivity and specificity for the expert. These plots demonstrate similarity in the numbering patterns produced by the system and the expert.