Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases

Acner Camino; Zhuo Wang; Jie Wang; Mark E. Pennesi; Paul Yang; David Huang; Dengwang Li; Yali Jia

doi:10.1364/BOE.9.003092

Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases

Acner Camino, Zhuo Wang, Jie Wang, Mark E. Pennesi, Paul Yang, David Huang, Dengwang Li, Yali Jia

Ophthalmology

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

The objective quantification of photoreceptor loss in inherited retinal degenerations (IRD) is essential for measuring disease progression, and is now especially important with the growing number of clinical trials. Optical coherence tomography (OCT) is a non-invasive imaging technology widely used to recognize and quantify such anomalies. Here, we implement a versatile method based on a convolutional neural network to segment the regions of preserved photoreceptors in two different IRDs (choroideremia and retinitis pigmentosa) from OCT images. An excellent segmentation accuracy (~90%) was achieved for both IRDs. Due to the flexibility of this technique, it has potential to be extended to additional IRDs in the future.

Original language	English (US)
Article number	#326568
Pages (from-to)	3092-3105
Number of pages	14
Journal	Biomedical Optics Express
Volume	9
Issue number	7
DOIs	https://doi.org/10.1364/BOE.9.003092
State	Published - Jul 1 2018

ASJC Scopus subject areas

Biotechnology
Atomic and Molecular Physics, and Optics

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Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases. / Camino, Acner; Wang, Zhuo; Wang, Jie; Pennesi, Mark E.; Yang, Paul ; Huang, David; Li, Dengwang; Jia, Yali.

In: Biomedical Optics Express, Vol. 9, No. 7, #326568, 01.07.2018, p. 3092-3105.

Research output: Contribution to journal › Article › peer-review

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abstract = "The objective quantification of photoreceptor loss in inherited retinal degenerations (IRD) is essential for measuring disease progression, and is now especially important with the growing number of clinical trials. Optical coherence tomography (OCT) is a non-invasive imaging technology widely used to recognize and quantify such anomalies. Here, we implement a versatile method based on a convolutional neural network to segment the regions of preserved photoreceptors in two different IRDs (choroideremia and retinitis pigmentosa) from OCT images. An excellent segmentation accuracy (~90%) was achieved for both IRDs. Due to the flexibility of this technique, it has potential to be extended to additional IRDs in the future.",

author = "Acner Camino and Zhuo Wang and Jie Wang and Pennesi, {Mark E.} and Paul Yang and David Huang and Dengwang Li and Yali Jia",

note = "Funding Information: National Institutes of Health (Bethesda, MD) (R01EY027833, DP3 DK104397, R01 EY024544, P30 EY010572); National Natural Science Foundation of China (NO. 61471226); Natural Science Foundation for Distinguished Young Scholars of Shandong Province (NO. JQ201516); China Scholarship Council, China (grant no.: 2016008370080); unrestricted departmental funding grant and William & Mary Greve Special Scholar Award from Research to Prevent Blindness (New York, NY). Funding Information: The authors also thank the support from Taishan scholar project of Shandong Province.",

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AU - Li, Dengwang

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