Detection and classification of 32 tooth types in dental panoramic radiographs using single CNN model and post-processing

Takumi Morishita; Chisako Muramatsu; Yuta Seino; Ryo Takahashi; Tatsuro Hayashi; Wataru Nishiyama; Xiangrong Zhou; Takeshi Hara; Akitoshi Katsumata; Hiroshi Fujita

doi:10.1117/12.2625961

30 April 2022 Detection and classification of 32 tooth types in dental panoramic radiographs using single CNN model and post-processing

Takumi Morishita, Chisako Muramatsu, Yuta Seino, Ryo Takahashi, Tatsuro Hayashi, Wataru Nishiyama, Xiangrong Zhou, Takeshi Hara, Akitoshi Katsumata, Hiroshi Fujita

Author Affiliations +

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121771T (2022) https://doi.org/10.1117/12.2625961
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

The purpose of this study is to analyze dental panoramic radiographs for completing dental files to contribute to the diagnosis by dentists. In this study, we recognized 32 tooth types and classified four tooth attributes (tooth, remaining root, pontic, and implant) using 925 dental panoramic radiographs. YOLOv4 and post-processing were used for the recognition of 32 tooth types. As a result, the tooth detection recall was 99.65%, the number of false positives was 0.10 per image, and the 32-type recognition recall was 98.55%. For the classification of the four tooth attributes, two methods were compared. In Method 1, image classification was performed using a clipped image based on the tooth detection result. In Method 2, the labels of tooth attributes were added to the labels of tooth types in object detection. By providing two labels for the same bounding box, we performed multi-label object detection. The accuracy of Method 1 was 0.995 and that of Method 2 was 0.990. Method 2 uses a simple and robust model yet has comparable accuracy as Method 1. In addition, Method 2 did not require additional CNN models. This suggested the usefulness of multi-label detection.

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Takumi Morishita, Chisako Muramatsu, Yuta Seino, Ryo Takahashi, Tatsuro Hayashi, Wataru Nishiyama, Xiangrong Zhou, Takeshi Hara, Akitoshi Katsumata, and Hiroshi Fujita "Detection and classification of 32 tooth types in dental panoramic radiographs using single CNN model and post-processing", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121771T (30 April 2022); https://doi.org/10.1117/12.2625961

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KEYWORDS

Teeth

Panoramic photography

Radiography

Image classification

Target detection

Dentistry

Image processing

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