Automated detection of dilated capillaries on optical coherence tomography angiography

Changlei Dongye, Miao Zhang, Thomas S. Hwang, Jie Wang, Simon S. Gao, Liang Liu, David Huang, David J. Wilson, Yali Jia

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Automated detection and grading of angiographic high-risk features in diabetic retinopathy can potentially enhance screening and clinical care. We have previously identified capillary dilation in angiograms of the deep plexus in optical coherence tomography angiography as a feature associated with severe diabetic retinopathy. In this study, we present an automated algorithm that uses hybrid contrast to distinguish angiograms with dilated capillaries from healthy controls and then applies saliency measurement to map the extent of the dilated capillary networks. The proposed algorithm agreed well with human grading.

Original languageEnglish (US)
Article number#279084
Pages (from-to)1101-1109
Number of pages9
JournalBiomedical Optics Express
Issue number2
StatePublished - Feb 1 2017


  • Image analysis
  • Image processing
  • Medical and biological imaging
  • Ophthalmology
  • Optical coherence tomography

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics


Dive into the research topics of 'Automated detection of dilated capillaries on optical coherence tomography angiography'. Together they form a unique fingerprint.

Cite this