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

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 -