Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding

Félix Fuentes-Hurtado; Sandra Morales; Jose M. Mossi; Valery Naranjo; Vadim Fedulov; David Woldbye; Kristian Klemp; Marie Torm; Michael Larsen

doi:10.1007/978-3-030-03493-1_4

Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding

Félix Fuentes-Hurtado^*, Sandra Morales, Jose M. Mossi, Valery Naranjo, Vadim Fedulov, David Woldbye, Kristian Klemp, Marie Torm, Michael Larsen

^*Corresponding author for this work

Neuropharm and Genetics

Abstract

Optical coherence tomography (OCT) is a useful technique to monitor retinal damage. We present an automatic method to accurately classify rodent OCT images in healthy and pathological (before and after 14 days of intravitreal injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results in a leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. The presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. The result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings
Editors	Hujun Yin, Paulo Novais, David Camacho, Antonio J. Tallón-Ballesteros
Number of pages	8
Publisher	Springer
Publication date	2018
Pages	27-34
ISBN (Print)	9783030034924
DOIs	https://doi.org/10.1007/978-3-030-03493-1_4
Publication status	Published - 2018
Event	19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 - Madrid, Spain Duration: 21 Nov 2018 → 23 Nov 2018

Conference

Conference	19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Country/Territory	Spain
City	Madrid
Period	21/11/2018 → 23/11/2018

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11314 LNCS
ISSN	0302-9743

Keywords

Deep-learning
Glaucoma
Optical coherence tomography

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-03493-1_4

Cite this

Fuentes-Hurtado, F., Morales, S., Mossi, J. M., Naranjo, V., Fedulov, V., Woldbye, D., Klemp, K., Torm, M., & Larsen, M. (2018). Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding. In H. Yin, P. Novais, D. Camacho, & A. J. Tallón-Ballesteros (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings (pp. 27-34). Springer. https://doi.org/10.1007/978-3-030-03493-1_4

Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding. / Fuentes-Hurtado, Félix; Morales, Sandra; Mossi, Jose M. et al.
Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. ed. / Hujun Yin; Paulo Novais; David Camacho; Antonio J. Tallón-Ballesteros. Springer, 2018. p. 27-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11314 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Fuentes-Hurtado, F, Morales, S, Mossi, JM, Naranjo, V, Fedulov, V, Woldbye, D , Klemp, K, Torm, M & Larsen, M 2018, Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding. in H Yin, P Novais, D Camacho & AJ Tallón-Ballesteros (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11314 LNCS, pp. 27-34, 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, Madrid, Spain, 21/11/2018. https://doi.org/10.1007/978-3-030-03493-1_4

Fuentes-Hurtado F, Morales S, Mossi JM, Naranjo V, Fedulov V, Woldbye D et al. Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding. In Yin H, Novais P, Camacho D, Tallón-Ballesteros AJ, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. Springer. 2018. p. 27-34. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11314 LNCS). doi: 10.1007/978-3-030-03493-1_4

Fuentes-Hurtado, Félix ; Morales, Sandra ; Mossi, Jose M. et al. / Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding. Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. editor / Hujun Yin ; Paulo Novais ; David Camacho ; Antonio J. Tallón-Ballesteros. Springer, 2018. pp. 27-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11314 LNCS).

@inproceedings{01756d4a3f344843b984b544acb2a3fa,

title = "Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding",

abstract = "Optical coherence tomography (OCT) is a useful technique to monitor retinal damage. We present an automatic method to accurately classify rodent OCT images in healthy and pathological (before and after 14 days of intravitreal injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results in a leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. The presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. The result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.",

keywords = "Deep-learning, Glaucoma, Optical coherence tomography",

author = "F{\'e}lix Fuentes-Hurtado and Sandra Morales and Mossi, {Jose M.} and Valery Naranjo and Vadim Fedulov and David Woldbye and Kristian Klemp and Marie Torm and Michael Larsen",

year = "2018",

doi = "10.1007/978-3-030-03493-1_4",

language = "English",

isbn = "9783030034924",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "27--34",

editor = "Hujun Yin and Paulo Novais and David Camacho and Tall{\'o}n-Ballesteros, {Antonio J.}",

booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings",

note = "19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 ; Conference date: 21-11-2018 Through 23-11-2018",

}

TY - GEN

T1 - Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding

AU - Fuentes-Hurtado, Félix

AU - Morales, Sandra

AU - Mossi, Jose M.

AU - Naranjo, Valery

AU - Fedulov, Vadim

AU - Woldbye, David

AU - Klemp, Kristian

AU - Torm, Marie

AU - Larsen, Michael

PY - 2018

Y1 - 2018

N2 - Optical coherence tomography (OCT) is a useful technique to monitor retinal damage. We present an automatic method to accurately classify rodent OCT images in healthy and pathological (before and after 14 days of intravitreal injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results in a leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. The presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. The result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.

AB - Optical coherence tomography (OCT) is a useful technique to monitor retinal damage. We present an automatic method to accurately classify rodent OCT images in healthy and pathological (before and after 14 days of intravitreal injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results in a leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. The presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. The result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.

KW - Deep-learning

KW - Glaucoma

KW - Optical coherence tomography

U2 - 10.1007/978-3-030-03493-1_4

DO - 10.1007/978-3-030-03493-1_4

M3 - Article in proceedings

AN - SCOPUS:85057091948

SN - 9783030034924

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 27

EP - 34

BT - Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings

A2 - Yin, Hujun

A2 - Novais, Paulo

A2 - Camacho, David

A2 - Tallón-Ballesteros, Antonio J.

PB - Springer

T2 - 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018

Y2 - 21 November 2018 through 23 November 2018

ER -

Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding

Abstract

Conference

Keywords

UN SDGs

Access to Document

Fingerprint

Cite this