Fluid region segmentation in OCT images based on convolution neural network

Dong Liu; Xiaoming Liu; Tianyu Fu; Zhou Yang

doi:10.1117/12.2282513

21 July 2017 Fluid region segmentation in OCT images based on convolution neural network

Dong Liu, Xiaoming Liu, Tianyu Fu, Zhou Yang

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104202A (2017) https://doi.org/10.1117/12.2282513
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

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.

Citation Download Citation

Dong Liu, Xiaoming Liu, Tianyu Fu, and Zhou Yang "Fluid region segmentation in OCT images based on convolution neural network", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202A (21 July 2017); https://doi.org/10.1117/12.2282513

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