June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Computer aided diagnosis of age-related macular degeneration in 3D OCT images by deep learning
Author Affiliations & Notes
  • Yalin Zheng
    Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
    St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, United Kingdom
  • Bryan M Williams
    Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
  • Harry Pratt
    Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
  • Baidaa Al-Bander
    Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, United Kingdom
    Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
  • Xiangqian Wu
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
  • Yitian Zhao
    School of Optics and Electronics, Beijing Institute of Technology, Beijing, China
    Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
  • Footnotes
    Commercial Relationships   Yalin Zheng, None; Bryan Williams, None; Harry Pratt, None; Baidaa Al-Bander, None; Xiangqian Wu, None; Yitian Zhao, None
  • Footnotes
    Support  NVIDIA Inc for sponsoring the K40 GPU card
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 824. doi:
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    • Get Citation

      Yalin Zheng, Bryan M Williams, Harry Pratt, Baidaa Al-Bander, Xiangqian Wu, Yitian Zhao; Computer aided diagnosis of age-related macular degeneration in 3D OCT images by deep learning. Invest. Ophthalmol. Vis. Sci. 2017;58(8):824.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Three-dimensional (3D) optical coherence tomography (OCT) images are increasingly used in the management of eye disease, yet there has been no corresponding increase in the availability of software tools to support the analysis of large amounts of 3D OCT data. We propose a new computer aided diagnosis (CADx) model based on deep learning for automatic diagnosis of eye disease in 3D OCT images and demonstrate its performance with an application to the diagnosis of age-related macular degeneration (AMD).

Methods : 384 3D spectral domain (SD)-OCT images (269 intermediate AMD and 115 normal eyes, one image per eye) from a public dataset provided by Duke University were used. 324 randomly chosen images (228 AMD and 96 normal) were used to train the deep learning CADx model while the remainder were reserved for testing the model. The VGG-M network was adopted here for the purpose of classification which comprises 5 convolution layers, 5 max pooling layers, and 3 full connection layers. A cross entropy function was used as the cost function to train the network. Drop-out (ratio 0.5) was used in order to reduce the problem of overfitting during training. The training images were augmented by applying horizontal flipping and random rotation (range 0-10 degrees) to the original image in order to improve classification performance. A class weight (1 to 3 with a step of 0.5) was applied to the normal class to alleviate classification problems associated with imbalanced datasets. Sensitivity, specificity and accuracy of classification were used to evaluate the performance of the trained model.

Results : The sensitivity, specificity and accuracy of the best model with data augmentation and class weighting were 0.927, 0.821 and 0.893 respectively. These results were significantly higher than those of the correspondence model without data augmentation (p<0.0001). The specificity (resp. sensitivity) value increases (resp. decreases) with the increase of the class weight. The model with a weighting value 2.5 yields the best accuracy (p<0.001 when compared to the model without class weighting).

Conclusions : Our results have demonstrated that deep learning based CADx models can provide very encouraging classification performance for the diagnosis of AMD. The developed model could be further developed and validated with large datasets in order to support the management of eye disease.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

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