Tooth labeling in cone-beam CT using deep convolutional neural network for forensic identification

Yuma Miki; Chisako Muramatsu; Tatsuro Hayashi; Xiangrong Zhou; Takeshi Hara; Akitoshi Katsumata; Hiroshi Fujita

doi:10.1117/12.2254332

3 March 2017 Tooth labeling in cone-beam CT using deep convolutional neural network for forensic identification

Yuma Miki, Chisako Muramatsu, Tatsuro Hayashi, Xiangrong Zhou, Takeshi Hara, Akitoshi Katsumata, Hiroshi Fujita

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101343E (2017) https://doi.org/10.1117/12.2254332
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

In large disasters, dental record plays an important role in forensic identification. However, filing dental charts for corpses is not an easy task for general dentists. Moreover, it is laborious and time-consuming work in cases of large scale disasters. We have been investigating a tooth labeling method on dental cone-beam CT images for the purpose of automatic filing of dental charts. In our method, individual tooth in CT images are detected and classified into seven tooth types using deep convolutional neural network. We employed the fully convolutional network using AlexNet architecture for detecting each tooth and applied our previous method using regular AlexNet for classifying the detected teeth into 7 tooth types. From 52 CT volumes obtained by two imaging systems, five images each were randomly selected as test data, and the remaining 42 cases were used as training data. The result showed the tooth detection accuracy of 77.4% with the average false detection of 5.8 per image. The result indicates the potential utility of the proposed method for automatic recording of dental information.

Citation Download Citation

Yuma Miki, Chisako Muramatsu, Tatsuro Hayashi, Xiangrong Zhou, Takeshi Hara, Akitoshi Katsumata, and Hiroshi Fujita "Tooth labeling in cone-beam CT using deep convolutional neural network for forensic identification", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343E (3 March 2017); https://doi.org/10.1117/12.2254332

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