OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes

Oscar Perdomo; Sebastian Otalora; Fabio A. Gonzalez; Fabrice Meriaudeau; Henning Muller

doi:10.1109/ISBI.2018.8363839

OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes

Oscar Perdomo, Sebastian Otalora, Fabio A. Gonzalez, Fabrice Meriaudeau, Henning Muller

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

22 Scopus citations

Abstract

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%.

Original language	English (US)
Title of host publication	2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
Publisher	IEEE Computer Society
Pages	1423-1426
Number of pages	4
ISBN (Electronic)	9781538636367
DOIs	https://doi.org/10.1109/ISBI.2018.8363839
State	Published - May 23 2018
Externally published	Yes
Event	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States Duration: Apr 4 2018 → Apr 7 2018

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2018-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country	United States
City	Washington
Period	4/4/18 → 4/7/18

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Radiology Nuclear Medicine and imaging

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

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10.1109/ISBI.2018.8363839

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Perdomo, O., Otalora, S., Gonzalez, F. A., Meriaudeau, F., & Muller, H. (2018). OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (pp. 1423-1426). (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363839

Perdomo, Oscar ; Otalora, Sebastian ; Gonzalez, Fabio A. ; Meriaudeau, Fabrice ; Muller, Henning. / OCT-NET : A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes. 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society, 2018. pp. 1423-1426 (Proceedings - International Symposium on Biomedical Imaging).

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title = "OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes",

abstract = "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%.",

author = "Oscar Perdomo and Sebastian Otalora and Gonzalez, {Fabio A.} and Fabrice Meriaudeau and Henning Muller",

note = "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: {\textcopyright} 2018 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 ; Conference date: 04-04-2018 Through 07-04-2018",

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doi = "10.1109/ISBI.2018.8363839",

language = "English (US)",

series = "Proceedings - International Symposium on Biomedical Imaging",

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Perdomo, O, Otalora, S, Gonzalez, FA, Meriaudeau, F & Muller, H 2018, OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes. in 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Proceedings - International Symposium on Biomedical Imaging, vol. 2018-April, IEEE Computer Society, pp. 1423-1426, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, United States, 4/4/18. https://doi.org/10.1109/ISBI.2018.8363839

OCT-NET : A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes. / Perdomo, Oscar; Otalora, Sebastian; Gonzalez, Fabio A.; Meriaudeau, Fabrice; Muller, Henning.

2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society, 2018. p. 1423-1426 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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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%.

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Perdomo O, Otalora S, Gonzalez FA, Meriaudeau F, Muller H. OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society. 2018. p. 1423-1426. (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2018.8363839

OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

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