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 Pennesi, Paul Yang, David Huang, Dengwang Li, Jia Yali

Research output: Contribution to journalArticle

3 Citations (Scopus)

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 languageEnglish (US)
Article number#326568
Pages (from-to)3092-3105
Number of pages14
JournalBiomedical Optics Express
Volume9
Issue number7
DOIs
StatePublished - Jul 1 2018

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Retinal Diseases
photoreceptors
Optical Coherence Tomography
learning
Choroideremia
tomography
Learning
Retinal Degeneration
Retinitis Pigmentosa
degeneration
progressions
Disease Progression
flexibility
Clinical Trials
anomalies
Technology

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

Cite this

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; Yang, Paul; Huang, David; Li, Dengwang; Yali, Jia.

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

Research output: Contribution to journalArticle

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