@inproceedings{ab2cd5f9261d408c90e5962a936bc1b4,
title = "Deepvessel: Retinal vessel segmentation via deep learning and conditional random field",
abstract = "Retinal vessel segmentation is a fundamental step for various ocular imaging applications. In this paper,we formulate the retinal vessel segmentation problem as a boundary detection task and solve it using a novel deep learning architecture. Our method is based on two key ideas: (1) applying a multi-scale and multi-level Convolutional Neural Network (CNN) with a side-output layer to learn a rich hierarchical representation,and (2) utilizing a Conditional Random Field (CRF) to model the long-range interactions between pixels. We combine the CNN and CRF layers into an integrated deep network called Deep Vessel. Our experiments show that the DeepVessel system achieves state-of-the-art retinal vessel segmentation performance on the DRIVE,STARE,and CHASE DB1 datasets with an efficient running time.",
author = "Huazhu Fu and Yanwu Xu and Stephen Lin and Wong, {Damon Wing Kee} and Jiang Liu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.",
year = "2016",
doi = "10.1007/978-3-319-46723-8_16",
language = "English",
isbn = "9783319467221",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "132--139",
editor = "Gozde Unal and Sebastian Ourselin and Leo Joskowicz and Sabuncu, {Mert R.} and William Wells",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",
address = "Germany",
}