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International Journal of Computer Assisted Radiology and Surgery

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03-01-2024 | Original Article

Rapid detection of non-normal teeth on dental X-ray images using improved Mask R-CNN with attention mechanism

Authors: Yanbin Guo, Jing Guo, Yong Li, Peng Zhang, Yuan-Di Zhao, Yundi Qiao, Benyuan Liu, Guoping Wang

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 4/2024

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Abstract

Purpose

Dental health has been getting increased attention. Timely detection of non-normal teeth (caries, residual root, retainer, teeth filling, etc.) is of great importance for people’s health, well-being, and quality of life. This work proposes a rapid detection of non-normal teeth based on improved Mask R-CNN, aiming to achieve comprehensive screening of non-normal teeth on dental X-ray images.

Methods

An improved Mask R-CNN based on attention mechanism was used to develop a non-normal teeth detection method trained on a high-quality annotated dataset, which can segment the whole mask of each non-normal tooth on the dental X-ray image immediately.

Results

The average precision (AP) of the proposed non-normal teeth detection was 0.795 with an intersection-over-union of 0.5 and max detections (maxDets) of 32, which was higher than that of the typical Mask R-CNN method (AP = 0.750). In addition, validation experiments showed that the evaluation metrics (AP, recall, precision-recall (P-R) curve) of the proposed method were superior to those of the Mask R-CNN method. Furthermore, the experimental results indicated that proposed method exhibited a high sensitivity (95.65%) in detecting secondary caries. The proposed method took about 0.12 s to segment non-normal teeth on one dental X-ray image using the laptop (8G memory, NVIDIA RTX 3060 graphics processing unit), which was much faster than conventional manual methods.

Conclusion

The proposed method enhances the accuracy and efficiency of abnormal tooth diagnosis for practitioners, while also facilitating early detection and treatment of dental caries to substantially lower patient costs. Additionally, it can enable rapid and objective evaluation of student performance in dental examinations.

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Title: Rapid detection of non-normal teeth on dental X-ray images using improved Mask R-CNN with attention mechanism
Authors: Yanbin Guo
Jing Guo
Yong Li
Peng Zhang
Yuan-Di Zhao
Yundi Qiao
Benyuan Liu
Guoping Wang
Publication date: 03-01-2024
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 4/2024
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-023-03047-1

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Abstract

Purpose

Methods

Results

Conclusion

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