PixelBNN: Augmenting the Pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation

TitlePixelBNN: Augmenting the Pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation
Publication TypeJournal Article
Year of Publication2019
AuthorsLeopold, H., J. Orchard, J. Zelek, and V. Lakshminarayanan
JournalJournal of Imaging
Volume5
KeywordsConvolutional networks, Deep learning, Image segmentation, Ophthalmic diagnosis, Ophthalmology, retina, Retinal Vessels
Abstract

Analysis of retinal fundus images is essential for eye-care physicians in the diagnosis, care and treatment of patients. Accurate fundus and/or retinal vessel maps give rise to longitudinal studies able to utilize multimedia image registration and disease/condition status measurements, as well as applications in surgery preparation and biometrics. The segmentation of retinal morphology has numerous applications in assessing ophthalmologic and cardiovascular disease pathologies. Computer-aided segmentation of the vasculature has proven to be a challenge, mainly due to inconsistencies such as noise and variations in hue and brightness that can greatly reduce the quality of fundus images. The goal of this work is to collate different key performance indicators (KPIs) and state-of-the-art methods applied to this task, frame computational efficiency-performance trade-offs under varying degrees of information loss using common datasets, and introduce PixelBNN, a highly efficient deep method for automating the segmentation of fundus morphologies. The model was trained, tested and cross tested on the DRIVE, STARE and CHASE_DB1 retinal vessel segmentation datasets. Performance was evaluated using G-mean, Mathews Correlation Coefficient and F1-score, with the main success measure being computation speed. The network was 8.5× faster than the current state-of-the-art at test time and performed comparatively well, considering a 5× to 19× reduction in information from resizing images during preprocessing. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

DOI10.3390/jimaging5020026