3D-CNN for Glaucoma Detection Using Optical Coherence Tomography

Yasmeen George, Bhavna Antony, Hiroshi Ishikawa, Gadi Wollstein, Joel Schuman, Rahil Garnavi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The large size of raw 3D optical coherence tomography (OCT) volumes poses challenges for deep learning methods as it cannot be accommodated on a single GPU in its original resolution. The direct analysis of these volumes however, provides advantages such as circumventing the need for the segmentation of retinal structures. Previously, a deep learning (DL) approach was proposed for the detection of glaucoma directly from 3D OCT volumes, where the volumes were significantly downsampled first. In this paper, we propose an end-to-end DL model for the detection of glaucoma that doubles the number of input voxels of the previously proposed method, and also boasts an improved AUC = 0.973 over the results obtained using the previously proposed approach of AUC = 0.946. Furthermore, this paper also includes a quantitative analysis of the regions of the volume highlighted by grad-CAM visualization. Occlusion of these highlighted regions resulted in a drop in performance by 40%, indicating that the regions highlighted by gradient-weighted class activation maps (grad-CAM) are indeed crucial to the performance of the model.

    Original languageEnglish (US)
    Title of host publicationOphthalmic Medical Image Analysis - 6th International Workshop, OMIA 2019, Held in Conjunction with MICCAI 2019, Proceedings
    EditorsHuazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
    PublisherSpringer
    Pages52-59
    Number of pages8
    ISBN (Print)9783030329556
    DOIs
    StatePublished - Jan 1 2019
    Event6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2019, held in conjunction with the 22nd International Conference on Medical Imaging Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
    Duration: Oct 17 2019Oct 17 2019

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11855 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2019, held in conjunction with the 22nd International Conference on Medical Imaging Computing and Computer-Assisted Intervention, MICCAI 2019
    CountryChina
    CityShenzhen
    Period10/17/1910/17/19

    Keywords

    • 3D-CNN
    • Glaucoma detection
    • Gradient-weighted class activation maps
    • Optical coherence tomography
    • Visual explanations

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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  • Cite this

    George, Y., Antony, B., Ishikawa, H., Wollstein, G., Schuman, J., & Garnavi, R. (2019). 3D-CNN for Glaucoma Detection Using Optical Coherence Tomography. In H. Fu, M. K. Garvin, T. MacGillivray, Y. Xu, & Y. Zheng (Eds.), Ophthalmic Medical Image Analysis - 6th International Workshop, OMIA 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 52-59). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11855 LNCS). Springer. https://doi.org/10.1007/978-3-030-32956-3_7