AUTHOR=Luo Dan , Zeng Wei , Chen Jinlong , Tang Wei 

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

JOURNAL=Frontiers in Medical Technology

VOLUME=Volume 3 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/medical-technology/articles/10.3389/fmedt.2021.767836

DOI=10.3389/fmedt.2021.767836

ISSN=2673-3129

ABSTRACT=Deep learning has become a research focus in the field of medical image analysis, especially in the automatic segmentation of the stomatological image, showing the great advance of segmentation performance. This paper systematically reviews the recent literature on segmentation methods of stomatological images via deep learning and their clinical applications, by categorizing them in different tasks and analyzing their advantages and disadvantages. The main categories we explore include the data sources, the backbone network, and the task formulation. For the data sources, this paper categorizes them into panoramic radiography, dental X-rays, CBCT, Multi-slice spiral CT, and intraoral scan image-based methods respectively. For the backbone network, this paper groups them into CNN-based and Transformer-based methods. For the task formulation, this paper divides them into semantic segmentation tasks and instance segmentation tasks. Toward the end of this paper, we also discuss the challenges and provide several further research orientations for the automatic segmentation of stomatological images.