June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Deep learning network for Glaucoma detection at 40 million voxels
Author Affiliations & Notes
  • Bhavna Josephine Antony
    IBM Research Australia, Southbank, Victoria, Australia
  • Hiroshi Ishikawa
    Department of Ophthalmology, NYU Langone Health, New York City, New York, United States
  • Gadi Wollstein
    Department of Ophthalmology, NYU Langone Health, New York City, New York, United States
  • Joel S Schuman
    Department of Ophthalmology, NYU Langone Health, New York City, New York, United States
    Department of Biomedical Engineering, NYU Tandon School of Engineering, New York City, New York, United States
  • Rahil Garnavi
    IBM Research Australia, Southbank, Victoria, Australia
  • Footnotes
    Commercial Relationships   Bhavna Antony, IBM Research (E); Hiroshi Ishikawa, None; Gadi Wollstein, None; Joel Schuman, Zeiss (P); Rahil Garnavi, IBM Research Australia (E)
  • Footnotes
    Support  R01-EY013178, Unrestricted grant by Research to Prevent Blindness
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 4528. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Bhavna Josephine Antony, Hiroshi Ishikawa, Gadi Wollstein, Joel S Schuman, Rahil Garnavi; Deep learning network for Glaucoma detection at 40 million voxels. Invest. Ophthalmol. Vis. Sci. 2020;61(7):4528.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : Current GPU memory limitations do not support the analysis of OCT scans at its original resolution, and previous techniques have downsampled the inputs considerably which resulted in a loss of detail. Here, we utilise a new memory management support framework that allows for the training of large deep learning networks and apply it to the detection of glaucoma in OCT scans at its original resolution.

Methods : A total of 1110 SDOCT volumes (Cirrus, Zeiss, CA) were acquired from both eyes of 624 subjects (139 healthy and 485 glaucomatous patients (POAG)). A convolutional neural network (CNN) consisting of 8 3D-convolutional layers with a total of 600K parameters and was trained using a cross-entropy loss to differentiate between the healthy and glaucomatous scans. To avoid GPU memory constraints, the network was trained using a large model support library that automatically adds swap-in and swap-out nodes for transferring tensors from GPUs to the host and vice versa. This allowed for the OCT scans to be analysed at the original resolution of 200x200x1024. The performance of the network was gauged by computing the area under the receiver operating characteristic (AUC) curve. The performance of this network was also compared to a previously proposed network that ingested downsampled OCT scans (50x50x128), consisted of 5 3D-convolutional layers and had a total of 222K parameters; and a machine-learning technique (random forests) that relied on segmented features (peripapillary nerve fibre thicknesses). Class activation maps (CAM) were also generated for each of these networks to provide a qualitative view of the regions that the network deemed as important and relevant to the task.

Results : The AUCs computed on the test set for the networks that analysed the volumes at the original and downsampled resolutions was found to be 0.92 and 0.91, respectively. The CAMs obtained using the high resolution images show more detail in comparison to the downsampled volume. The random forest technique showed an AUC of 0.85.

Conclusions : The performance of the two networks was comparable for glaucoma detection but showed a vast improvement over the random forest that relied on segmented features. The ability to retain detail (as shown in the CAM) will likely allow for improvements in other tasks, such as spatial correspondences between visual field test locations and retinal structure.

This is a 2020 ARVO Annual Meeting abstract.

 

CAM on the downsampled scan

CAM on the downsampled scan

 

CAM on the original OCT scan

CAM on the original OCT scan

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×