Scale-space approximated convolutional neural networks for retinal vessel segmentation.

Journal: Computer methods and programs in biomedicine

Published Date: Jun 29, 2019

Abstract

BACKGROUND AND OBJECTIVE: Retinal fundus images are widely used to diagnose retinal diseases and can potentially be used for early diagnosis and prevention of chronic vascular diseases and diabetes. While various automatic retinal vessel segmentation methods using deep learning have been proposed, they are mostly based on common CNN structures developed for other tasks such as classification.

Authors

Kyoung Jin Noh

Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi-do 13620, South Korea.
Sang Jun Park

Department of Ophthalmology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
Soochahn Lee

Department of Electronic Engineering, Soonchunhyang University, Asan-si, Republic of Korea. Electronic address: soochahn.lee@gmail.com.

Keywords

Algorithms Area Under Curve Deep Learning False Positive Reactions Fundus Oculi Humans Image Processing, Computer-Assisted Neural Networks, Computer Normal Distribution Retinal Diseases Retinal Vessels ROC Curve Signal Processing, Computer-Assisted

External Resources

View on PubMed Access via DOI PubMed (31416552)

Scale-space approximated convolutional neural networks for retinal vessel segmentation.

Abstract

Authors

Keywords

External Resources

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