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 af dette arbejde
4 Citationer (Scopus)

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.

OriginalsprogEngelsk
Publikationsdato1 jan. 2018
Antal sider8
DOI
StatusUdgivet - 1 jan. 2018
Begivenhed19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 - Madrid, Spanien
Varighed: 21 nov. 201823 nov. 2018

Konference

Konference19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Land/OmrådeSpanien
ByMadrid
Periode21/11/201823/11/2018

Fingeraftryk

Dyk ned i forskningsemnerne om 'Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding'. Sammen danner de et unikt fingeraftryk.

Citationsformater