NDLI: CPGAN: Conditional patch-based generative adversarial network for retinal vessel segmentation

Content Provider	IET Digital Library
Author	Rammy, Sadaqat Ali Abbas, Waseem Hassan, Naqy Ul Raza, Asif Zhang, Wu
Abstract	Retinal blood vessels, the diagnostic bio-marker of ophthalmologic and diabetic retinopathy, utilise thick and thin vessels for diagnostic and monitoring purposes. The existing deep learning methods attempt to segment the retinal vessels using a unified loss function. However, a difference in spatial features of thick and thin vessels and a biased distribution creates an imbalanced thickness, rendering the unified loss function to be useful only for thick vessels. To address this challenge, a patch-based generative adversarial network-based technique is proposed which iteratively learns both thick and thin vessels in fundoscopic images. It introduces an additional loss function that allows the generator network to learn thin and thick vessels, while the discriminator network assists in segmenting out both vessels as a combined objective function. Compared with state-of-the-art techniques, the proposed model demonstrates the enhanced accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves on STARE, DRIVE, and CHASEDB1 datasets.
Starting Page	1081
Ending Page	1090
Page Count	10
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 6, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1007
Journal	IET Image Processing
Publisher Date	2019-11-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biased Distribution Biology And Medical Computing Biomedical Imaging/measurement Biomedical Optical Imaging Blood Vessels Computer Vision And Image Processing Technique Conditional Patch-based Generative Adversarial Network CPGAN Deep Learning Method Diabetic Retinopathy Diagnostic Biomarker Eye Fundoscopic Image Generator Network Image Segmentation Learning in AI Medical Image Processing Neural Computing Technique Ophthalmologic Retinopathy Optical And Laser Radiation Optical, Image And Video Signal Processing Patch-based Generative Adversarial Network-based Technique Patient Diagnostic Method And Instrumentation Receiver Operating Characteristic Curves Retinal Blood Vessels Retinal Vessel Segmentation Sensitivity Analysis Spatial Feature Unified Loss Function
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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