Title
TSASNet: Tooth segmentation on dental panoramic X-ray images by Two-Stage Attention Segmentation Network
Abstract
Tooth segmentation acts as a crucial and fundamental role in dentistry for doctors to make diagnosis and treatment plans. In this paper, we propose a Two-Stage Attention Segmentation Network (TSASNet) on dental panoramic X-ray images to address the issues suffered in the tooth boundary and tooth root segmentation task which are caused by the low contrast and uneven intensity distribution. We firstly adopt an attention model which is embedded with global and local attention modules to roughly localize the tooth region in the first stage. Without any interactive operator, the attention model so constructed can automatically aggregate pixel-wise contextual information and identify coarse tooth boundaries. To better obtain final boundary information, we use a fully convolutional network as the second stage to further segment the real tooth area from the attention maps obtained from the first stage. The effectiveness of TSASNet is substantiated on the benchmark dataset containing 1,500 dental panoramic X-ray images, our proposed method achieves 96.94% of accuracy, 92.72% of dice and 93.77% of recall, significantly superior to the current state-of-the-art methods.
Year
DOI
Venue
2020
10.1016/j.knosys.2020.106338
Knowledge-Based Systems
Keywords
DocType
Volume
Panoramic X-ray image,Attention model,Tooth segmentation
Journal
206
ISSN
Citations 
PageRank 
0950-7051
5
0.43
References 
Authors
0
7
Name
Order
Citations
PageRank
Yue Zhao15828.59
Pengcheng Li261.79
Chenqiang Gao3238.86
Yang Liu450.43
Qiaoyi Chen561.12
Feng Yang661.79
Deyu Meng72025105.31