MEDnet, a neural network for automated detection of avascular area in OCT angiography

Yukun Guo, Acner Camino, Jie Wang, David Huang, Thomas S. Hwang, Yali Jia

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


Screening and assessing diabetic retinopathy (DR) are essential for reducing morbidity associated with diabetes. Macular ischemia is known to correlate with the severity of retinopathy. Recent studies have shown that optical coherence tomography angiography (OCTA), with intrinsic contrast from blood flow motion, is well suited for quantified analysis of the avascular area, which is potentially a useful biomarker in DR. In this study, we propose the first deep learning solution to segment the avascular area in OCTA of DR. The network design consists of a multi-scaled encoder-decoder neural network (MEDnet) to detect the non-perfusion area in 6 × 6 mm2 and in ultra-wide field retinal angiograms. Avascular areas were effectively detected in DR subjects of various disease stages as well as in the foveal avascular zone of healthy subjects.

Original languageEnglish (US)
Article number#341420
Pages (from-to)5147-5158
Number of pages12
JournalBiomedical Optics Express
Issue number11
StatePublished - Nov 1 2018

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics


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