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Tooth instance segmentation from cone-beam CT images through point-based detection and Gaussian disentanglement

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Abstract

Individual tooth segmentation and identification from cone-beam computed tomography images are preoperative prerequisites for orthodontic treatments. Instance segmentation methods using convolutional neural networks have demonstrated ground-breaking results on individual tooth segmentation tasks, and are used in various medical imaging applications. While point-based detection networks achieve superior results on dental images, it is still a challenging task to distinguish adjacent teeth because of their similar topologies and proximate nature. In this study, we propose a point-based tooth localization network that effectively disentangles each individual tooth based on a Gaussian disentanglement objective function. The proposed network first performs heatmap regression accompanied by box regression for all the anatomical teeth. A novel Gaussian disentanglement penalty is employed by minimizing the sum of the pixel-wise multiplication of the heatmaps for all adjacent teeth pairs. Subsequently, individual tooth segmentation is performed by converting a pixel-wise labeling task to a distance map regression task to minimize false positives in adjacent regions of the teeth. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches by increasing the average precision of detection by 9.1%, which results in a high performance in terms of individual tooth segmentation. The primary significance of the proposed method is two-fold: (1) the introduction of a point-based tooth detection framework that does not require additional classification and (2) the design of a novel loss function that effectively separates Gaussian distributions based on heatmap responses in the point-based detection framework.

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Acknowledgements

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2021-0-00511, Robust AI and Distributed Attack Detection for Edge AI Security).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea

    Jusang Lee, Minkyung Lee & Yeong-Gil Shin

  2. School of Software, Soongsil University, 369 Sangdo-ro, Dongjak-gu, 06978, Seoul, South Korea

    Minyoung Chung

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  1. Jusang Lee
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Correspondence to Minyoung Chung.

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All authors including Jusang Lee, Minyoung Chung, Minkyung Lee, and Yeong-Gil Shin agreed the submission of this manuscript.

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Lee, J., Chung, M., Lee, M. et al. Tooth instance segmentation from cone-beam CT images through point-based detection and Gaussian disentanglement. Multimed Tools Appl 81, 18327–18342 (2022). https://doi.org/10.1007/s11042-022-12524-9

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  • DOI: https://doi.org/10.1007/s11042-022-12524-9

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