Paper Abstract and Keywords |
Presentation |
2020-01-29 13:20
Tooth detection based on relation of teeth with Relation Module on dental Cone-beam CT Shota Kutsuna (Gifu Univ.), Chisako Muramatsu (Shiga Univ.), Ryo Takahashi, Tatsuro Hayashi (Media CO., Ltd.), Xiangrong Zhor (Gifu Univ.), Wataru nishiyama (Asashi Univ.), Yoshiko Ariji (Aichi-Gakuin Univ.), Takeshi Hara (Gifu Univ.), Akitoshi Katsumata (Asashi Univ.), Eichiro Ariji (Aichi-Gakuin Univ.), Hiroshi Fujita (Gifu Univ.) MI2019-82 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Recently, image diagnosis using dental panoramic X-ray images and dental cone-beam CT (CBCT) is widely used in dental treatment. CBCT accurately represents the anatomical information and is used for diagnosis and treatment planning. In this study, we aim to automatically recognize teeth to assist doctors in interpretation. The order of the dentition is an important information for recognizing the tooth type. Therefore, we consider the relation module that takes into account the relationship between objects is effective for this study. Based on the features extracted from the input images by using CNN, the relation module outputs the box regression results and their tooth types. Using 84 cases, we evaluated the detection performance by cross-validation. As a result, the high detection rate over 90% was obtained. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Dental CT / Deep Learning / Object Detection / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 399, MI2019-82, pp. 75-76, Jan. 2020. |
Paper # |
MI2019-82 |
Date of Issue |
2020-01-22 (MI) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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MI2019-82 |
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