Towards automatic glaucoma assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images

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

Optical coherence tomography (OCT) is an important imaging modality that is used frequently to monitor the state of retinal layers both in humans and animals. Automated OCT analysis in rodents is an important method to study the possible toxic effect of treatments before the test in humans. In this paper, an automatic method to detect the most significant retinal layers in rat OCT images is presented. This algorithm is based on an encoder-decoder fully convolutional network (FCN) architecture combined with a robust method of post-processing. After the validation, it was demonstrated that the proposed method outperforms the commercial Insight image segmentation software. We obtained results (averaged absolute distance error) in the test set for the training database of 2.52 ± 0.80 µm. In the predictions done by the method, in a different database (only used for testing), we also achieve the promising results of 4.45 ± 3.02 µm.

Original languageEnglish
Title of host publicationEUSIPCO 2019 - 27th European Signal Processing Conference
PublisherEuropean Signal Processing Conference, EUSIPCO
Publication dateSep 2019
ISBN (Electronic)9789082797039
Publication statusPublished - Sep 2019
Event27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Spain
Duration: 2 Sep 20196 Sep 2019


Conference27th European Signal Processing Conference, EUSIPCO 2019
ByA Coruna
Sponsoret al., National Science Foundation (NSF), Office of Naval Research Global (ONR), Turismo A Coruna, Oficina de Informacion Turismo de A Coruna, Xunta de Galicia, Centro de Investigacion TIC (CITIC), Xunta de Galicia, Conselleria de Cultura, Educacion e Ordenacion Universitaria
SeriesEuropean Signal Processing Conference

    Research areas

  • Convolutional neural network, Glaucoma assessment, Layer segmentation, Optical coherence tomography, Rodent OCT

ID: 241088792