Advanced deep learning for blood vessel segmentation in retinal fundus images

Lua Ngo, Jae Ho Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication5th International Winter Conference on Brain-Computer Interface, BCI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages91-92
Number of pages2
ISBN (Electronic)9781509050963
DOIs
Publication statusPublished - 2017 Feb 16
Event5th International Winter Conference on Brain-Computer Interface, BCI 2017 - Gangwon Province, Korea, Republic of
Duration: 2017 Jan 92017 Jan 11

Other

Other5th International Winter Conference on Brain-Computer Interface, BCI 2017
CountryKorea, Republic of
CityGangwon Province
Period17/1/917/1/11

Keywords

  • Biomedical optical imaging
  • Blood vessels
  • Fundus images
  • Image segmentation
  • Medical image processing

ASJC Scopus subject areas

  • Signal Processing
  • Human-Computer Interaction

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  • Cite this

    Ngo, L., & Han, J. H. (2017). Advanced deep learning for blood vessel segmentation in retinal fundus images. In 5th International Winter Conference on Brain-Computer Interface, BCI 2017 (pp. 91-92). [7858169] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2017.7858169