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Semi or fully automatic tooth segmentation in CBCT images: a review.

Authors :
Zheng, Qianhan
Gao, Yu
Zhou, Mengqi
Li, Huimin
Lin, Jiaqi
Zhang, Weifang
Chen, Xuepeng
Source :
PeerJ Computer Science; Apr2024, p1-34, 34p
Publication Year :
2024

Abstract

Cone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical practice. Recently, with advancements in computer vision technology, scholars have conducted in-depth research, proposing various fast and accurate tooth segmentation methods. In this review, we review 55 articles in this field and discuss the effectiveness, advantages, and disadvantages of each approach. In addition to simple classification and discussion, this review aims to reveal how tooth segmentation methods can be improved by the application and refinement of existing image segmentation algorithms to solve problems such as irregular morphology and fuzzy boundaries of teeth. It is assumed that with the optimization of these methods, manual operation will be reduced, and greater accuracy and robustness in tooth segmentation will be achieved. Finally, we highlight the challenges that still exist in this field and provide prospects for future directions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
23765992
Database :
Complementary Index
Journal :
PeerJ Computer Science
Publication Type :
Academic Journal
Accession number :
177325819
Full Text :
https://doi.org/10.7717/peerj-cs.1994