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J. Imaging 2019, 5(2), 26; https://doi.org/10.3390/jimaging5020026

PixelBNN: Augmenting the PixelCNN with Batch Normalization and the Presentation of a Fast Architecture for Retinal Vessel Segmentation

1
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
2
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 1 November 2018 / Revised: 5 January 2019 / Accepted: 24 January 2019 / Published: 2 February 2019
(This article belongs to the Special Issue Mathematical and Computational Methods in Image Processing)
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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. View Full-Text
Keywords: convolutional networks; deep learning; retinal vessels; image segmentation; ophthalmology; retina; ophthalmic diagnosis convolutional networks; deep learning; retinal vessels; image segmentation; ophthalmology; retina; ophthalmic diagnosis
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Leopold, H.A.; Orchard, J.; Zelek, J.S.; Lakshminarayanan, V. PixelBNN: Augmenting the PixelCNN with Batch Normalization and the Presentation of a Fast Architecture for Retinal Vessel Segmentation. J. Imaging 2019, 5, 26.

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