Classifying DME vs normal SD-OCT volumes: A review

This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a...

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Bibliographic Details
Published in2016 23rd International Conference on Pattern Recognition (ICPR) pp. 1297 - 1302
Main Authors Massich, Joan, Rastgoo, Mojdeh, Lemaitre, Guillaume, Cheung, Carol Y., Wong, Tien Y., Sidibe, Desire, Meriaudeau, Fabrice
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
benchmark
Diabetes
Diabetic Macular Edema (DME)
Feature extraction
Histograms
Machine Learning (ML)
Pathology
Principal component analysis
Retina
Spectral Domain OCT (SD-OCT)
Testing
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DOI10.1109/ICPR.2016.7899816

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Summary:This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this common benchmark and dataset to produce reliable comparison.
DOI:10.1109/ICPR.2016.7899816