@inproceedings{871245db7578471e86850119b790257f,
title = "A deep neural network for vessel segmentation of Scanning Laser Ophthalmoscopy images",
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.",
keywords = "Retinal vessel segmentation, Scanning Laser Ophthalmoscopy",
author = "Meyer, \{Maria Ines\} and Pedro Costa and Adrian Galdran and Mendon{\c c}a, \{Ana Maria\} and Aur{\'e}lio Campilho",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 14th International Conference on Image Analysis and Recognition, ICIAR 2017 ; Conference date: 05-07-2017 Through 07-07-2017",
year = "2017",
doi = "10.1007/978-3-319-59876-5\_56",
language = "English",
isbn = "9783319598758",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "507--515",
editor = "Farida Cheriet and Fakhri Karray and Aurelio Campilho",
booktitle = "Image Analysis and Recognition - 14th International Conference, ICIAR 2017, Proceedings",
address = "Germany",
}