Production of Synthetic Dataset for Optical Coherence Tomography Images with Age-Related Macular Edema

Document Type : Research Paper

Authors

1 Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Isfahan Eye Research Center, Department of Ophthalmology, Isfahan University of Medical Sciences, Isfahan, Iran

3 School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. Research Associate, Newcastle University, UK

Abstract

Introduction: Today, retinal optical coherence tomography (OCT) plays an important role in ophthalmology and automated analysis of OCT images is very important, The need for labeled data and manual segmentation of these data by the ophthalmologist and limited labeled data are challenges in the field of medical imaging and a large number of labeled data is needed to train deep learning algorithms as well as to evaluate the performance of image processing algorithms.
Methods: The data of this project include OCT dataset in the presence of retinal abnormality in age-related macular degeneration disease, obtained from AREDS2 Ancillary device at Duke University. Seventy B scans that are randomly selected from the above set are used to train the algorithm. The active shape model is used to generate synthetic boundaries, and new images are produced similar to the shapes in the training set.
Results: Considering the anatomical features of the existing images such as the number and thickness of the layers as well as the brightness related to them, data with high similarity are produced in the presence of anomalies. Synthetic data were reviewed by an ophthalmologist to evaluate the results.
Conclusion: The proposed model is designed to fill the gap in the production of OCT Synthetic data in the presence of anomalies, using important features such as the boundary of the main layers of the retina and disease-damaged tissue, and can be used as a data set for training deep learning-based algorithms as well as performance of segmentation algorithms.

Keywords

References

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medical journal of mashhad university of medical sciences
Volume 65, Issue 2
May and June 2022
Pages 493-504

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