@inproceedings{817e387a848249aeae99c3b6bf684a51,
title = "Automatic Retinal Blood Vessel Segmentation Based on Multi-Level Convolutional Neural Network",
abstract = "Since morphology of retinal blood vessels plays a key role in ophthalmological disease diagnosis, the automatic retinal blood segmentation method is essential for computer-aided diagnosis system. In this paper, a supervised method which is based on multi-level convolutional neural network is proposed to separate blood vessels from fundus image. By using both local and global feature extractors, small vessels can be well distinguished and global spatial consistency of the image can be ensured. Meanwhile, unsupervised pre-processing and postprocessing methods are applied to achieve better segmentation results. Experiment results on public database show that the proposed method outperforms the state-of-the-art performance (AUC up to >0.978) on DRIVE database.",
keywords = "Convolutional neural network, computer-aided diagnosis, deep learning, retinal vessel segmentation",
author = "Jinnan Guo and Shiwei Ren and Yueting Shi and Haoyu Wang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018 ; Conference date: 13-10-2018 Through 15-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/CISP-BMEI.2018.8633115",
language = "English",
series = "Proceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Wei Li and Qingli Li and Lipo Wang",
booktitle = "Proceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018",
address = "United States",
}