Dr. Yankui Sun Profile

Dr. Yankui Sun

at Tsinghua University

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Author

Publications (4)

SPIE Journal Paper | 16 September 2020 Open Access

Automatic diagnosis of macular diseases from OCT volume based on its two-dimensional feature map and convolutional neural network with attention mechanism

Yankui Sun, Haoran Zhang, Xianlin Yao

JBO, Vol. 25, Issue 09, 096004, (September 2020) https://doi.org/10.1117/12.10.1117/1.JBO.25.9.096004

KEYWORDS: Optical coherence tomography, Image classification, Feature extraction, Visualization, RGB color model, Convolutional neural networks, Visual process modeling, Surface plasmons, 3D image processing, Convolution

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Significance: Automatic and accurate classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images is essential for assisting ophthalmologist in the diagnosis and grading of macular diseases. Therefore, more effective OCT volume classification for automatic recognition of macular diseases is needed. Aim: For OCT volumes in which only OCT volume-level labels are known, OCT volume classifiers based on its global feature and deep learning are designed, validated, and compared with other methods. Approach: We present a general framework to classify OCT volume for automatic recognizing macular diseases. The architecture of the framework consists of three modules: B-scan feature extractor, two-dimensional (2-D) feature map generation, and volume-level classifier. Our architecture could address OCT volume classification using two 2-D image machine learning classification algorithms. Specifically, a convolutional neural network (CNN) model is trained and used as a B-scan feature extractor to construct a 2-D feature map of an OCT volume and volume-level classifiers such as support vector machine and CNN with/without attention mechanism for 2-D feature maps are described. Results: Our proposed methods are validated on the publicly available Duke dataset, which consists of 269 intermediate age-related macular degeneration (AMD) volumes and 115 normal volumes. Fivefold cross-validation was done, and average accuracy, sensitivity, and specificity of 98.17%, 99.26%, and 95.65%, respectively, are achieved. The experiments show that our methods outperform the state-of-the-art methods. Our methods are also validated on our private clinical OCT volume dataset, consisting of 448 AMD volumes and 462 diabetic macular edema volumes. Conclusions: We present a general framework of OCT volume classification based on its 2-D feature map and CNN with attention mechanism and describe its implementation schemes. Our proposed methods could classify OCT volumes automatically and effectively with high accuracy, and they are a potential practical tool for screening of ophthalmic diseases from OCT volume.

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SPIE Journal Paper | 20 May 2019 Open Access

Automatic detection of retinal regions using fully convolutional networks for diagnosis of abnormal maculae in optical coherence tomography images

Zhongyang Sun, Yankui Sun

JBO, Vol. 24, Issue 05, 056003, (May 2019) https://doi.org/10.1117/12.10.1117/1.JBO.24.5.056003

KEYWORDS: Optical coherence tomography, Image segmentation, Image classification, Retina, Sun, Performance modeling, Denoising, Image processing, Data modeling, Tissues

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Proceedings Article | 29 March 2016 Paper

Automatic segmentation of canine retinal OCT using adaptive gradient enhancement and region growing

Yufan He, Yankui Sun, Min Chen, Yuanjie Zheng, Hui Liu, Cecilia Leon, William Beltran, James Gee

Proceedings Volume 9788, 97881Q (2016) https://doi.org/10.1117/12.2217186

KEYWORDS: Image segmentation, Optical coherence tomography, Retina, Therapeutics, Clinical trials, Image resolution, Digital filtering, Image filtering, Algorithm development, Analytical research

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SPIE Journal Paper | 1 September 2009 Open Access

Method for optical coherence tomography image classification using local features and earth mover's distance

Yankui Sun, Ming Lei

JBO, Vol. 14, Issue 05, 054037, (September 2009) https://doi.org/10.1117/12.10.1117/1.3251059

KEYWORDS: Optical coherence tomography, Image classification, Skin, Sun, Image analysis, Tissues, Principal component analysis, Image retrieval, Image resolution, Computer science

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