@inproceedings{657fea7749f44c51ab0f2a1888fb90df,
title = "Fluid region segmentation in OCT images based on convolution neural network",
abstract = "In the retinal image, characteristics of fluid have great significance for diagnosis in eye disease. In the clinical, the segmentation of fluid is usually conducted manually, but is time-consuming and the accuracy is highly depend on the expert's experience. In this paper, we proposed a segmentation method based on convolution neural network (CNN) for segmenting the fluid from fundus image. The B-scans of OCT are segmented into layers, and patches from specific region with annotation are used for training. After the data set being divided into training set and test set, network training is performed and a good segmentation result is obtained, which has a significant advantage over traditional methods such as threshold method.",
keywords = "convolution neural network, fluid region, optical coherence tomography",
author = "Dong Liu and Xiaoming Liu and Tianyu Fu and Zhou Yang",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; 9th International Conference on Digital Image Processing, ICDIP 2017 ; Conference date: 19-05-2017 Through 22-05-2017",
year = "2017",
doi = "10.1117/12.2282513",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Xudong Jiang and Falco, {Charles M.}",
booktitle = "Ninth International Conference on Digital Image Processing, ICDIP 2017",
address = "United States",
}