Abstract
The quantitative analysis of retinal blood vessels is important for the management of vascular disease and tackling problems such as locating blood clots. Such tasks are hampered by the inability to accurately trace back problems along vessels to the source. This is due to the unresolved challenge of distinguishing automatically between vessel branchings and vessel crossings. In this paper, we present a new technique for tackling this challenging problem by developing a convolutional neural network approach for first locating vessel junctions and then classifying them as either branchings or crossings. We achieve a high accuracy of 94% for junction detection and 88% for classification. Combined with work in segmentation, this method has the potential to facilitate automated localisation of blood clots and other disease symptoms leading to improved management of eye disease through aiding or replacing a clinicians diagnosis.
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Acknowledgement
H. Pratt acknowledges PhD funding from Fight for Sight charity. This project is funded in part by the National Institute for Health Research’s i4i Programme. This paper summarises independent research funded by the National Institute for Health Research (NIHR) under its i4i Programme (Grant Reference Number II-LA-0813-20005). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
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Pratt, H., Williams, B.M., Ku, J., Coenen, F., Zheng, Y. (2017). Automatic Detection and Identification of Retinal Vessel Junctions in Colour Fundus Photography. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_3
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DOI: https://doi.org/10.1007/978-3-319-60964-5_3
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