6533b859fe1ef96bd12b6e6f
RESEARCH PRODUCT
Classifying DME vs Normal SD-OCT volumes: A review
Carol Y. CheungMojdeh RastgooJoan MassichGuillaume LemaitreDésiré SidibéFabrice MeriaudeauTien Yin Wongsubject
genetic structuresComputer scienceDiabetic macular edemaEarly detection[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingMachine learningcomputer.software_genre01 natural sciences010309 optics03 medical and health sciences0302 clinical medicinebenchmark0103 physical sciencesmedicine[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingRetinaBlindnessbusiness.industryMachine Learning (ML)medicine.diseaseeye diseasesSpectral Domain OCT (SD-OCT)medicine.anatomical_structure030221 ophthalmology & optometryBenchmark (computing)Artificial intelligenceData miningsense organsDiabetic Macular Edema (DME)businesscomputer[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingdescription
International audience; 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-12-04 |