Tooth instance segmentation from cone-beam CT images through point-based detection and Gaussian disentanglement
- Authors
- Lee, Jusang; Chung, Minyoung; Lee, Minkyung; Shin, Yeong-Gil
- Issue Date
- May-2022
- Publisher
- SPRINGER
- Keywords
- Distance-based segmentation; Gaussian disentanglement loss; Instance segmentation; Point-based object detection; Tooth CBCT segmentation
- Citation
- MULTIMEDIA TOOLS AND APPLICATIONS, v.81, no.13, pp.18327 - 18342
- Journal Title
- MULTIMEDIA TOOLS AND APPLICATIONS
- Volume
- 81
- Number
- 13
- Start Page
- 18327
- End Page
- 18342
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/42354
- DOI
- 10.1007/s11042-022-12524-9
- ISSN
- 1380-7501
- 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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