Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks.


Journal Article

Imaging with an adaptive optics scanning light ophthalmoscope (AOSLO) enables direct visualization of the cone photoreceptor mosaic in the living human retina. Quantitative analysis of AOSLO images typically requires manual grading, which is time consuming, and subjective; thus, automated algorithms are highly desirable. Previously developed automated methods are often reliant on ad hoc rules that may not be transferable between different imaging modalities or retinal locations. In this work, we present a convolutional neural network (CNN) based method for cone detection that learns features of interest directly from training data. This cone-identifying algorithm was trained and validated on separate data sets of confocal and split detector AOSLO images with results showing performance that closely mimics the gold standard manual process. Further, without any need for algorithmic modifications for a specific AOSLO imaging system, our fully-automated multi-modality CNN-based cone detection method resulted in comparable results to previous automatic cone segmentation methods which utilized ad hoc rules for different applications. We have made free open-source software for the proposed method and the corresponding training and testing datasets available online.

Full Text

Duke Authors

Cited Authors

  • Cunefare, D; Fang, L; Cooper, RF; Dubra, A; Carroll, J; Farsiu, S

Published Date

  • July 26, 2017

Published In

Volume / Issue

  • 7 / 1

Start / End Page

  • 6620 -

PubMed ID

  • 28747737

Pubmed Central ID

  • 28747737

Electronic International Standard Serial Number (EISSN)

  • 2045-2322

International Standard Serial Number (ISSN)

  • 2045-2322

Digital Object Identifier (DOI)

  • 10.1038/s41598-017-07103-0


  • eng