Paper Info
Title
Computer Aided Annotation of Early Esophageal Cancer in Gastroscopic Images based on Deeplabv3+ Network
Abstract
The diagnoses of Early Esophageal Cancer (EEC) based on gastroscopic images is a challenging task in clinic, which relies heavily on subjective artificial detection and annotation. As a result, computer aided diagnosis (CAD) methods that support the clinicians become highly attractive. In this paper, we proposed a CAD method which realized the automatic detection and annotation of EEC lesions in gastroscopic images. The proposed method initially utilized an advanced Deep Learning (DL) network Deeplabv3+ to obtain a preliminary prediction of EEC regions. Then, a post-processing step which referenced the clinical requirements was designed and applied to get the final annotation results. Totally 3190 gastroscopic images of 732 patients were used in this work. The final experimental results show that the EEC detection rate of our method was 97.07%, and the mean Dice Similarity Coefficient (DSC) was 74.01%, which are higher than those of other state-of-the-are DL-based methods. In addition, the false positive output of our method is fewer. Therefore, the proposed method offers a good potential to aid the clinical diagnoses of EEC.
Year
DOI
Venue
2019
10.1145/3354031.3354046
Proceedings of the 2019 4th International Conference on Biomedical Signal and Image Processing (ICBIP 2019)
Keywords
Field
DocType
Deep learning, Early esophageal cancer, Gastroscopic image, Lesion annotation
Esophageal cancer,Annotation,Computer-aided,Computer science,Medical physics
Conference
ISBN
Citations 
PageRank 
978-1-4503-7224-4
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Ding Yun Liu100.34
Hong Xiu Jiang200.34
Nini Rao38511.36
Cheng Si Luo400.34
Wen Ju Du500.34
Zheng Wen Li600.34
Tao Gan7111.86