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Title: A Deep Neural Network for Vessel Segmentation of Scanning Laser Ophthalmoscopy Images
Authors: Maria Inês Meyer
Adrian Galdran
Ana Maria Mendonça
Aurélio Campilho
Issue Date: 2017
Abstract: Retinal vessel segmentation is a fundamental and well-studied problem in the retinal image analysis field. The standard images in this context are color photographs acquired with standard fundus cameras. Several vessel segmentation techniques have been proposed in the literature that perform successfully on this class of images. However, for other retinal imaging modalities, blood vessel extraction has not been thoroughly explored. In this paper, we propose a vessel segmentation technique for Scanning Laser Opthalmoscopy (SLO) retinal images. Our method adapts a Deep Neural Network (DNN) architecture initially devised for segmentation of biological images (U-Net), to perform the task of vessel segmentation. The model was trained on a recent public dataset of SLO images. Results show that our approach efficiently segments the vessel network, achieving a performance that outperforms the current state-of-the-art on this particular class of images. © Springer International Publishing AG 2017.
metadata.dc.type: conferenceObject
Appears in Collections:C-BER - Articles in International Conferences

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