Medical College of Wisconsin
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Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks. Sci Rep 2017 Jul 26;7(1):6620

Date

07/28/2017

Pubmed ID

28747737

Pubmed Central ID

PMC5529414

DOI

10.1038/s41598-017-07103-0

Scopus ID

2-s2.0-85026398315 (requires institutional sign-in at Scopus site)   78 Citations

Abstract

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.

Author List

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

Authors

Joseph J. Carroll PhD Director, Professor in the Ophthalmology and Visual Sciences department at Medical College of Wisconsin
Robert F. Cooper Ph.D Assistant Professor in the Biomedical Engineering department at Marquette University




MESH terms used to index this publication - Major topics in bold

Automation
Humans
Image Processing, Computer-Assisted
Ophthalmoscopy
Retina
Retinal Cone Photoreceptor Cells
Software