Classifying DME vs Normal SD-OCT volumes: A review

被引：0
|
作者：
Massich, Joan [1 ]
Rastgoo, Mojdeh [1 ]
Lemaitre, Guillaume [1 ]
Cheung, Carol Y. [2 ]
Wong, Tien Y. [2 ]
Sidibe, Desire [1 ]
Meriaudeau, Fabrice [1 ,3 ]
机构：
[1] Univ Bourgogne Franche Comte, LE2I UMR6306, CNRS, Arts & Metiers, 12 Rue Fonderie, F-71200 Le Creusot, France
[2] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore
[3] Univ Teknol Petronas, CISIR, Elect & Elect Engn Dept, Seri Iskandar 32610, Perak, Malaysia
来源：
2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) | 2016年
关键词：
Diabetic Macular Edema (DME); Spectral Domain OCT (SD-OCT); Machine Learning (ML); benchmark; MACULAR DEGENERATION; SEGMENTATION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：1297 / 1302
页数：6
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