Retinal vessel segmentation via a Multi-resolution Contextual Network and adversarial learning.

Autor: Khan TM; School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia. Electronic address: tariq045@gmail.com., Naqvi SS; Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan., Robles-Kelly A; School of Information Technology, Faculty of Science Engineering & Built Environment, Deakin University, Locked Bag 20000, Geelong, Australia; Defence Science and Technology Group, 5111, Edinburgh, SA, Australia., Razzak I; School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
Jazyk: angličtina
Zdroj: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2023 Aug; Vol. 165, pp. 310-320. Date of Electronic Publication: 2023 Jun 02.
DOI: 10.1016/j.neunet.2023.05.029
Abstrakt: Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness. Accurate retinal vessel segmentation plays an important role in disease progression and diagnosis of such vision-threatening diseases. To this end, we propose a Multi-resolution Contextual Network (MRC-Net) that addresses these issues by extracting multi-scale features to learn contextual dependencies between semantically different features and using bi-directional recurrent learning to model former-latter and latter-former dependencies. Another key idea is training in adversarial settings for foreground segmentation improvement through optimization of the region-based scores. This novel strategy boosts the performance of the segmentation network in terms of the Dice score (and correspondingly Jaccard index) while keeping the number of trainable parameters comparatively low. We have evaluated our method on three benchmark datasets, including DRIVE, STARE, and CHASE, demonstrating its superior performance as compared with competitive approaches elsewhere in the literature.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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