Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images

Pathological conditions of the retina examined through regular screening [1], [2] can heavily assist prevention of visual blindness. Fundus imaging is the most widely used modality for early screening and detection of such blindness causing diseases like diabetic retinopathy, glucoma, agerelated macular degeneration [3], hypertension and stroke induced changes [4]. Imaging of fundus has largely improved with progress from the film based photography camera to use of electronic imaging sensors; as well as red free imaging, stereo photography, hyperspectral imaging, angiography, etc. [5], thereby reducing inter-and intra-observer reporting variability. Retinal image analysis has also significantly contributed to this technological development [5], [6]. Since fundus imaging is predominantly used for first level of abnormality screening, research focus includes: (i) detection and segmentation of retinal structures (vessels, fovea, optic disc), (ii) segmentation of abnormalities, and (iii) quality quantification of images acquired to assess reporting fitness [5]. Related Work: The process of clinical reporting of retinal abnormalities is systematic and lesions are reported with respect to their location from vessels or optic disc. Computer assisted diagnosis systems are accordingly being developed to improve the clinical workflow [5].