TY - GEN
T1 - OCT-NET
T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
AU - Perdomo, Oscar
AU - Otalora, Sebastian
AU - Gonzalez, Fabio A.
AU - Meriaudeau, Fabrice
AU - Muller, Henning
N1 - Funding Information:
Oscar Perdomo thanks COLCIENCIAS and HES-SO for funding this research with a doctoral grant and an international internship respectively. We appreciate the efforts devoted by Carol Cheung and Tien Y Wong from the Chinese University of Hong Kong (CUHK) and Singapore Eye Research Institute (SERI) to collect SD-OCT volumes.
Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/5/23
Y1 - 2018/5/23
N2 - Diabetic macular edema (DME) is one of the most common eye complication caused by diabetes mellitus, resulting in partial or total loss of vision. Optical Coherence Tomography (OCT) volumes have been widely used to diagnose different eye diseases, thanks to their sensitivity to represent small amounts of fluid, thickness between layers and swelling. However, the lack of tools for automatic image analysis for supporting disease diagnosis is still a problem. Convolutional neural networks (CNNs) have shown outstanding performance when applied to several medical images analysis tasks. This paper presents a model, OCT-NET, based on a CNN for the automatic classification of OCT volumes. The model was evaluated on a dataset of OCT volumes for DME diagnosis using a leave-one-out cross-validation strategy obtaining an accuracy, sensitivity, and specificity of 93.75%.
AB - Diabetic macular edema (DME) is one of the most common eye complication caused by diabetes mellitus, resulting in partial or total loss of vision. Optical Coherence Tomography (OCT) volumes have been widely used to diagnose different eye diseases, thanks to their sensitivity to represent small amounts of fluid, thickness between layers and swelling. However, the lack of tools for automatic image analysis for supporting disease diagnosis is still a problem. Convolutional neural networks (CNNs) have shown outstanding performance when applied to several medical images analysis tasks. This paper presents a model, OCT-NET, based on a CNN for the automatic classification of OCT volumes. The model was evaluated on a dataset of OCT volumes for DME diagnosis using a leave-one-out cross-validation strategy obtaining an accuracy, sensitivity, and specificity of 93.75%.
UR - http://www.scopus.com/inward/record.url?scp=85048129422&partnerID=8YFLogxK
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U2 - 10.1109/ISBI.2018.8363839
DO - 10.1109/ISBI.2018.8363839
M3 - Conference contribution
AN - SCOPUS:85048129422
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1423
EP - 1426
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PB - IEEE Computer Society
Y2 - 4 April 2018 through 7 April 2018
ER -
