July 2018
Volume 59, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2018
Assisting glaucoma diagnosis with optical coherence tomography and color fundus images using machine learning approach
Author Affiliations & Notes
  • Maiko Abe
    Department of ophthalmology,Graduate School of Medicine,Tohoku University, Sendai, Miyagi, Japan
  • Kazuko Omodaka
    Department of ophthalmology,Graduate School of Medicine,Tohoku University, Sendai, Miyagi, Japan
  • Guangzhou An
    R&D Division,TOPCON Corporation, Tokyo, Japan
    Cloud-Based Eye Disease Diagnosis Joint Research Team,RIKEN, Tokyo, Japan
  • Tsutomu Kikawa
    R&D Division,TOPCON Corporation, Tokyo, Japan
  • Masahiro Akiba
    R&D Division,TOPCON Corporation, Tokyo, Japan
    Cloud-Based Eye Disease Diagnosis Joint Research Team,RIKEN, Tokyo, Japan
  • Hideo Yokota
    Cloud-Based Eye Disease Diagnosis Joint Research Team,RIKEN, Tokyo, Japan
    Image Processing Research Team,RIKEN, Saitama, Japan
  • Toru Nakazawa
    Department of ophthalmology,Graduate School of Medicine,Tohoku University, Sendai, Miyagi, Japan
    Image Processing Research Team,RIKEN, Saitama, Japan
  • Footnotes
    Commercial Relationships   Maiko Abe, None; Kazuko Omodaka, Santen (F), Senjyu (F), Topcon (F), Wakamoto (F); Guangzhou An, Topcon (E); Tsutomu Kikawa, Topcon (E); Masahiro Akiba, Topcon (E); Hideo Yokota, None; Toru Nakazawa, Alcon (F), Kowa (F), NIDEK (F), Otsuka (F), Santen (F), Senjyu (F), Topcon (F), Wakamoto (F)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 2079. doi:
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      Maiko Abe, Kazuko Omodaka, Guangzhou An, Tsutomu Kikawa, Masahiro Akiba, Hideo Yokota, Toru Nakazawa; Assisting glaucoma diagnosis with optical coherence tomography and color fundus images using machine learning approach. Invest. Ophthalmol. Vis. Sci. 2018;59(9):2079.

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

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Abstract

Purpose : The purpose of this study was to develop a machine learning classification model to automatically classify glaucoma patients from normal subjects, with optical coherence tomography (OCT) and color fundus images.

Methods :
This study included 124 open-angle glaucoma and 180 normal eyes, and all those eyes were captured with spectral-domain OCT (3D OCT-2000, Topcon), in protocol of macular and disc 3 dimensional (3D) scans, as well as color fundus images.
With the exsting OCT segmentation sofware, we created retinal nerve fiber layer (RNFL) thickness and deviation map from disc 3D scans, and ganglion cell complex (GCC) thicknees and deviation map from macular 3D scans. With the created images from randomly selected subjects for training (n=204), we trained 5 separate machine learning classification model, and validated them with remaining data (n=100); 1) random forest (RF) for the color fundus images (green channel), 2) convolutional neural network (CNN) for disc RNFL thickness maps, 3) CNN for macular GCC thickness maps, 4) support vector machine (SVM) for disc RNFL deviation maps, 5) SVM for macular GCC deviation maps. Features for model 1) were extracted from a CNN model pre-trained on ImageNet, while for 4) and 5), features were created by non-negative matrix factorization method. Finally, another SVM model was trained to combine the five separate models to get a better classification accuracy, with the confidence from each model as the input.

Results : The validated accuracy for each machine learning model was as below; 1) 88.0%, 2) 90.0%, 3) 91.0%, 4) 92.0%, 5) 92.0%, while for combining the results from five separate model, the accuracy was improved to 96.0%.

Conclusions : This novel machine learning model was able to classify normal and glaucoma based on OCT and color fundus images with high and stable accuracy, and might be used in assisting glaucoma diagnosis.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

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