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A Novel Choroid Segmentation Method for Retinal Diagnosis Using Deep Learning

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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A Novel Choroid Segmentation Method for Retinal Diagnosis Using Deep Learning. / Al-Bander, Baidaa; Williams, Bryan M.; Al-Taee, Majid A. et al.
2017 10th International Conference on Developments in eSystems Engineering (DeSE). IEEE, 2017. p. 182-187.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Al-Bander, B, Williams, BM, Al-Taee, MA, Al-Nuaimy, W & Zheng, Y 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. https://doi.org/10.1109/DeSE.2017.37

APA

Al-Bander, B., Williams, B. M., Al-Taee, M. A., Al-Nuaimy, W., & Zheng, Y. (2017). A Novel Choroid Segmentation Method for Retinal Diagnosis Using Deep Learning. In 2017 10th International Conference on Developments in eSystems Engineering (DeSE) (pp. 182-187). IEEE. https://doi.org/10.1109/DeSE.2017.37

Vancouver

Al-Bander B, Williams BM, Al-Taee MA, Al-Nuaimy W, Zheng Y. A Novel Choroid Segmentation Method for Retinal Diagnosis Using Deep Learning. In 2017 10th International Conference on Developments in eSystems Engineering (DeSE). IEEE. 2017. p. 182-187 doi: 10.1109/DeSE.2017.37

Author

Al-Bander, Baidaa ; Williams, Bryan M. ; Al-Taee, Majid A. et al. / A Novel Choroid Segmentation Method for Retinal Diagnosis Using Deep Learning. 2017 10th International Conference on Developments in eSystems Engineering (DeSE). IEEE, 2017. pp. 182-187

Bibtex

@inproceedings{d49a44380c5544348d5fe9cf57a4b56c,
title = "A Novel Choroid Segmentation Method for Retinal Diagnosis Using Deep Learning",
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%).",
keywords = "Choroid, convolutional neural networks, deep learning, enhancement, segmentation",
author = "Baidaa Al-Bander and Williams, {Bryan M.} and Al-Taee, {Majid A.} and Waleed Al-Nuaimy and Yalin Zheng",
year = "2017",
month = jun,
day = "14",
doi = "10.1109/DeSE.2017.37",
language = "English",
pages = "182--187",
booktitle = "2017 10th International Conference on Developments in eSystems Engineering (DeSE)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - A Novel Choroid Segmentation Method for Retinal Diagnosis Using Deep Learning

AU - Al-Bander, Baidaa

AU - Williams, Bryan M.

AU - Al-Taee, Majid A.

AU - Al-Nuaimy, Waleed

AU - Zheng, Yalin

PY - 2017/6/14

Y1 - 2017/6/14

N2 - 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%).

AB - 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%).

KW - Choroid

KW - convolutional neural networks

KW - deep learning

KW - enhancement

KW - segmentation

U2 - 10.1109/DeSE.2017.37

DO - 10.1109/DeSE.2017.37

M3 - Conference contribution/Paper

SP - 182

EP - 187

BT - 2017 10th International Conference on Developments in eSystems Engineering (DeSE)

PB - IEEE

ER -