Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort. (1st November 2017)

• Record Type: Journal Article
• Authors: Welikala, R.A.; Foster, P.J.; Whincup, P.H.; Rudnicka, A.R.; Owen, C.G.; Strachan, D.P.; Barman, S.A.
• Is Part Of: Computers in biology and medicine. Volume 90(2017)
• Page Start: 23
• Page End: 32
• Publication Date: 2017-11-01
• Subjects: Retinal images -- Arteriole/venule classification -- Deep learning -- Convolutional neural networks -- UK Biobank -- Epidemiological studies; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.09.005
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The morphometric characteristics of the retinal vasculature are associated with future risk of many systemic and vascular diseases. However, analysis of data from large population based studies is needed to help resolve uncertainties in some of these associations. This requires automated systems that extract quantitative measures of vessel morphology from large numbers of retinal images. Associations between retinal vessel morphology and disease precursors/outcomes may be similar or opposing for arterioles and venules. Therefore, the accurate detection of the vessel type is an important element in such automated systems. This paper presents a deep learning approach for the automatic classification of arterioles and venules across the entire retinal image, including vessels located at the optic disc. This comprises of a convolutional neural network whose architecture contains six learned layers: three convolutional and three fully-connected. Complex patterns are automatically learnt from the data, which avoids the use of hand crafted features. The method is developed and evaluated using 835, 914 centreline pixels derived from 100 retinal images selected from the 135, 867 retinal images obtained at the UK Biobank (large population-based cohort study of middle aged and older adults) baseline examination. This is a challenging dataset in respect to image quality and hence arteriole/venule classification is required to be highly robust. The method achieves a significant increase in accuracy of 8.1% when compared to the baseline method, resulting in an arteriole/venule classification accuracy of 86.97% (per pixel basis) over the entire retinal image. Highlights: Morphology of retinal vasculature associated with risk markers of many diseases. Large population based studies help to resolve uncertainties in these associations. Automated system extracts morphometric data from the UK Biobank cohort study. Detection of the vessel type is an important element in such automated systems. Deep learning approach for the automatic classification of arterioles and venules.