Paper Info
Title
Automated Retinal Layer Segmentation of OCT Images Using Two-Stage FCN and Decision Mask
Abstract
Optical coherence tomography (OCT) is the standard method of generating high resolution retinal images, which inform retinal disease diagnosis and guide management. However, in order to fully extract and utilize the retinal information from the OCT images, automatic OCT segmentation is essential. Although neural networks have achieved great success with automatic segmentation, only using one neural network model to segment may lead to an ambiguous information problem where the result contains incorrect classification. In this paper, we propose a two-stage fully convolutional network (FCN) method to address these shortcomings. The OCT image is segmented in the first stage via a trained FCN, and in the second stage, the segmentation result is refined via another trained model with a decision mask to improve the segmentation performance. Therefore, two neural network models are trained sequentially to achieve better segmentation performance. The proposed method is evaluated using the publicly available Duke OCT dataset using the F1-score as the metric to measure the performance. The experimental results confirm the improvements of the proposed method.
Year
DOI
Venue
2019
10.1109/ISSPIT47144.2019.9001884
2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
Keywords
Field
DocType
OCT segmentation,two-stage,fully convolutional network,decision mask
Computer vision,Optical coherence tomography,Pattern recognition,Computer science,Segmentation,Artificial intelligence,Information problem,Retinal,Artificial neural network
Conference
ISSN
ISBN
Citations 
2162-7843
978-1-7281-5342-1
0
PageRank 
References 
Authors
0.34
5
7
Name
Order
Citations
PageRank
Yang Sun14615.21
Zeyu Fu293.62
Scott Stainton300.34
Shaun Barney400.34
Jeffry Hogg500.34
William Innes600.34
Satnam Singh Dlay700.34