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A fundus image usually contains the optic disc, pathologies and otherstructures in addition to vessels to be segmented. This study proposes a deepnetwork for vessel segmentation, whose architecture is inspired by inceptionmodules. The network contains three sub-networks, each with a different filtersize, which are connected in the last layer of the proposed network. Accordingto experiments conducted in the DRIVE and IOSTAR, the performance of ournetwork is found to be better than or comparable to that of the previousmethods. We also observe that the sub-networks pay attention to different partsof an input image when producing an output map in the last layer of theproposed network; though, training of the proposed network is not constrainedfor this purpose.

An Inception Inspired Deep Network to Analyse Fundus Images