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ToothNet: Automatic Tooth Instance Segmentation and Identification From Cone Beam CT Images

Zhiming Cui, Changjian Li, Wenping Wang
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
This paper proposes a method that uses deep convolutional neural networks to achieve automatic and accurate tooth instance segmentation and identification from CBCT (cone beam CT) images for digital dentistry  ...  To the best of our knowledge, our method is the first to use neural networks to achieve automatic tooth segmentation and identification from CBCT images.  ...  Lei Yang for proofreading, and Dr. Jian Shi for the valuable discussions. This work is supported by Hong Kong INNOVATION AND TECHNOLOGY FUND (ITF) (ITS/411/17FX).  ... 
doi:10.1109/cvpr.2019.00653 dblp:conf/cvpr/CuiLW19 fatcat:zkooy4zbnbhufgakljzz3zsumu

Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation [article]

Minyoung Chung, Minkyung Lee, Jioh Hong, Sanguk Park, Jusang Lee, Jingyu Lee, Jeongjin Lee, Yeong-Gil Shin
2020 arXiv   pre-print
Individual tooth segmentation from cone beam computed tomography (CBCT) images is an essential prerequisite for an anatomical understanding of orthodontic structures in several applications, such as tooth  ...  Our method comprises of three steps: 1) image cropping and realignment by pose regressions, 2) metal-robust individual tooth detection, and 3) segmentation.  ...  Index Terms-Cone beam computed tomography image segmentation, pose-aware tooth detection, pose regression neural network, tooth instance segmentation. I.  ... 
arXiv:2002.02143v1 fatcat:4kpz2aoxejcwdnnnaoqxvsqaxq

A fully automated method for 3D individual tooth identification and segmentation in dental CBCT [article]

Tae Jun Jang, Kang Cheol Kim, Hyun Cheol Cho, Jin Keun Seo
2021 arXiv   pre-print
Accurate and automatic segmentation of three-dimensional (3D) individual teeth from cone-beam computerized tomography (CBCT) images is a challenging problem because of the difficulty in separating an individual  ...  Thus, this paper proposes a fully automated method of identifying and segmenting 3D individual teeth from dental CBCT images.  ...  We would like to express our deepest gratitude HDXWILL which shares dental CBCT images and ground-truth data.  ... 
arXiv:2102.06060v1 fatcat:piwk4gftivfedm7uyfvaijx334

Deep Learning-Based Three-Dimensional Oral Conical Beam Computed Tomography for Diagnosis

Yangdong Lin, Miao He, Balakrishnan Nagaraj
2021 Journal of Healthcare Engineering  
By correcting the equation of R value and CBCT image vertical magnification rate, the magnification error of tooth image length could be reduced from 7.4.  ...  The segmentation results show that the proposed segmentation method can effectively segment the independent teeth in CBCT images, and the vertical magnification error of tooth CBCT images is clear.  ...  Fan et al. proposed a deep CNN-based automatic tooth instance segmentation method, called ToothNet framework, whose network structure consists of two networks. e first network extracts the edge image from  ... 
doi:10.1155/2021/4676316 pmid:34594483 pmcid:PMC8478532 fatcat:ckdn3hxpqrb5hi4q4d7jao2vay

Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application

Dan Luo, Wei Zeng, Jinlong Chen, Wei Tang
2021 Frontiers in Medical Technology  
We divided task formulations into semantic segmentation tasks and instance segmentation tasks.  ...  We categorized data sources into panoramic radiography, dental X-rays, cone-beam computed tomography, multi-slice spiral computed tomography, and methods based on intraoral scan images.  ...  AUTHOR CONTRIBUTIONS DL, WZ, JC, and WT contributed to the conception and design of the study. DL wrote the first draft of the manuscript.  ... 
doi:10.3389/fmedt.2021.767836 pmid:35047964 pmcid:PMC8757832 fatcat:gnbf4xxfh5hgdif6ynne3umiqi