Analysis of retinal fundus images is essential for physicians, optometrists and ophthalmologists in the diagnosis, care and treatment of patients. The first step of almost all forms of automated fundus analysis begins with the segmentation and subtraction of the retinal vasculature, while analysis of that same structure can aid in the diagnosis of certain retinal and cardiovascular conditions, such as diabetes or stroke. This paper investigates the use of a Convolutional Neural Network as a multi-channel classifier of retinal vessels using DRIVE, a database of fundus images. The result of the network with the application of a confidence threshold was slightly below the 2nd observer and gold standard, with an accuracy of 0.9419 and ROC of 0.9707. The output of the network with on post-processing boasted the highest sensitivity found in the literature with a score of 0.9568 and a good ROC score of 0.9689. The high sensitivity of the system makes it suitable for longitudinal morphology assessments, disease detection and other similar tasks.
Henry A. Leopold, Jeff Orchard, John Zelek, and Vasudevan Lakshminarayanan, "Segmentation and feature extraction of retinal vascular morphology," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330V (Presented at SPIE Medical Imaging: February 13, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2253744.
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