A Novel Choroid Segmentation Method for Retinal Diagnosis Using Deep Learning

Al-Bander, Baidaa and Williams, Bryan M. and Al-Taee, Majid A. and Al-Nuaimy, Waleed and Zheng, Yalin (2017) A Novel Choroid Segmentation Method for Retinal Diagnosis Using Deep Learning. In: 2017 10th International Conference on Developments in eSystems Engineering (DeSE). IEEE, pp. 182-187.

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Abstract

Reliable choroid measurements have become an important diagnostic modality for sight-threatening retinal diseases. However, automatic and accurate segmentation of the choroid remains an unresolved challenge. This paper proposes a novel choroid segmentation method, based on a deep learning algorithm that is capable of quick and accurate image segmentation without user intervention. This is achieved through combining pixel clustering, image enhancement and deep learning. The simple linear iterative clustering (SLIC) algorithm has been applied to extract the superpixels (patches). Next, the extracted patches are then enhanced through increasing contrast of the region of interest. After that, the patches are fed to convolutional neural network for labelling the regions into choroid or non-choroid. Performance of the developed algorithm is assessed using a dataset of 169 enhanced depth imaging optical coherence tomography images. The obtained results demonstrated effectiveness of the proposed segmentation method in terms of accuracy (98.01%).

Item Type:
Contribution in Book/Report/Proceedings
Subjects:
ID Code:
136720
Deposited By:
Deposited On:
21 Oct 2019 10:20
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Apr 2020 08:11