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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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 languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings
EditorsHujun Yin, Paulo Novais, David Camacho, Antonio J. Tallón-Ballesteros
Number of pages8
Publication date2018
ISBN (Print)9783030034924
Publication statusPublished - 2018
Event19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 - Madrid, Spain
Duration: 21 Nov 201823 Nov 2018


Conference19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11314 LNCS

    Research areas

  • Deep-learning, Glaucoma, Optical coherence tomography

ID: 209802521