3D-CNN for Glaucoma Detection Using Optical Coherence Tomography

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

doi:10.1007/978-3-030-32956-3_7

3D-CNN for Glaucoma Detection Using Optical Coherence Tomography

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

Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Ophthalmic Medical Image Analysis - 6th International Workshop, OMIA 2019, Held in Conjunction with MICCAI 2019, Proceedings
Editors	Huazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
Publisher	Springer
Pages	52-59
Number of pages	8
ISBN (Print)	9783030329556
DOIs	https://doi.org/10.1007/978-3-030-32956-3_7
State	Published - 2019
Event	6th 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 2019 → Oct 17 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11855 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th 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
Country/Territory	China
City	Shenzhen
Period	10/17/19 → 10/17/19

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)

Access to Document

10.1007/978-3-030-32956-3_7

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

3D-CNN for Glaucoma Detection Using Optical Coherence Tomography. / George, Yasmeen; Antony, Bhavna; Ishikawa, Hiroshi et al.

Ophthalmic Medical Image Analysis - 6th International Workshop, OMIA 2019, Held in Conjunction with MICCAI 2019, Proceedings. ed. / Huazhu Fu; Mona K. Garvin; Tom MacGillivray; Yanwu Xu; Yalin Zheng. Springer, 2019. p. 52-59 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11855 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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, MK Garvin, T MacGillivray, Y Xu & Y Zheng (eds), Ophthalmic Medical Image Analysis - 6th International Workshop, OMIA 2019, Held in Conjunction with MICCAI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11855 LNCS, Springer, pp. 52-59, 6th 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, 10/17/19. https://doi.org/10.1007/978-3-030-32956-3_7

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

George, Yasmeen ; Antony, Bhavna ; Ishikawa, Hiroshi et al. / 3D-CNN for Glaucoma Detection Using Optical Coherence Tomography. Ophthalmic Medical Image Analysis - 6th International Workshop, OMIA 2019, Held in Conjunction with MICCAI 2019, Proceedings. editor / Huazhu Fu ; Mona K. Garvin ; Tom MacGillivray ; Yanwu Xu ; Yalin Zheng. Springer, 2019. pp. 52-59 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AB - 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.

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3D-CNN for Glaucoma Detection Using Optical Coherence Tomography

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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