Please use this identifier to cite or link to this item:
Title: Ensemble Learning Approach to Retinal Thickness Assessment in Optical Coherence Tomography
Authors: Cazanas-Gordon, Alex 
Parra-Mora, Esther 
Cruz, Luis Alberto da Silva 
Keywords: Deep learning; ensemble learning; semantic segmentation; image processing; retinal thickness; optical coherence tomography
Issue Date: 2021
Project: FCT UIDB/EEA/50008/2020 
Secretariat of Higher Education, Science, Technology and Innovation of the Republic of Ecuador 
Serial title, monograph or event: IEEE Access
Volume: 9
Abstract: Manual assessment of the retinal thickness in optical coherence tomography images is a timeconsuming task, prone to error and inter-observer variability. The wide variability of the retinal appearance makes the automation of retinal image processing a challenging problem to tackle. The dif culty is even more accentuated in practice when the retinal tissue exhibits large structural changes due to disruptive pathology. In this work, we propose an ensemble-learning-based method for the automated segmentation of retinal boundaries in optical coherence tomography images that is robust to retinal abnormalities. The segmentation accuracy of the proposed algorithm was evaluated on two publicly available datasets that included cases of severe retinal edema. Moreover, the performance of the proposed method was compared to two existing methods, widely referenced in the relevant literature. The proposed algorithm outperformed reference methods at segmenting the retinal boundaries in both normal and pathological images. Furthermore, a thorough reliability analysis showed a strong agreement between the retinal thickness measurements derived from the segmentation obtained with the proposed method and corresponding manual measurements computed with the manual annotations.
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2021.3076427
Rights: openAccess
Appears in Collections:I&D IT - Artigos em Revistas Internacionais
FCTUC Eng.Electrotécnica - Artigos em Revistas Internacionais

Show full item record


checked on Nov 17, 2022


checked on Nov 15, 2022

Page view(s)

checked on Mar 27, 2023


checked on Mar 27, 2023

Google ScholarTM




This item is licensed under a Creative Commons License Creative Commons