@inproceedings{3160187f25eb46749aa3f3cf71a2d277,
title = "Advanced deep learning for blood vessel segmentation in retinal fundus images",
abstract = "Rising of deep learning methodologies draws huge attention to their application in image processing and classification. Catching up the trends, this study briefly presents state-of-The-Art of deep learning applications in medical imaging interfered with achievements of blood vessel segmentation methods in neurosensory retinal fundus images. Successful segmentation based on deep learning offers advantage in diagnosing ophthalmological disease or pathology.",
keywords = "Biomedical optical imaging, Blood vessels, Fundus images, Image segmentation, Medical image processing",
author = "Lua Ngo and Han, {Jae Ho}",
note = "Funding Information: This work was supported in part by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-R2720-16-0007) supervised by the IITP (Institute for Information & communications Technology Promotion). This research was also supported in part by Korea University Future Research Grant.; 5th International Winter Conference on Brain-Computer Interface, BCI 2017 ; Conference date: 09-01-2017 Through 11-01-2017",
year = "2017",
month = feb,
day = "16",
doi = "10.1109/IWW-BCI.2017.7858169",
language = "English",
series = "5th International Winter Conference on Brain-Computer Interface, BCI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "91--92",
booktitle = "5th International Winter Conference on Brain-Computer Interface, BCI 2017",
}