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Synthetic Oct Data in Challenging Conditions: Three-Dimensional Oct and Presence of Abnormalities Publisher Pubmed



Danesh H1 ; Maghooli K1 ; Dehghani A2 ; Kafieh R3, 4
Authors
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Authors Affiliations
  1. 1. Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
  2. 2. Isfahan Eye Research Center, Department of Ophthalmology, Isfahan University Of Medical Sciences, Isfahan, Iran
  3. 3. School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
  4. 4. Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom

Source: Medical and Biological Engineering and Computing Published:2022


Abstract

Nowadays, retinal optical coherence tomography (OCT) plays an important role in ophthalmology and automatic analysis of the OCT is of real importance: image denoising facilitates a better diagnosis and image segmentation and classification are undeniably critical in treatment evaluation. Synthetic OCT was recently considered to provide a benchmark for quantitative comparison of automatic algorithms and to be utilized in the training stage of novel solutions based on deep learning. Due to complicated data structure in retinal OCTs, a limited number of delineated OCT datasets are already available in presence of abnormalities; furthermore, the intrinsic three-dimensional (3D) structure of OCT is ignored in many public 2D datasets. We propose a new synthetic method, applicable to 3D data and feasible in presence of abnormalities like diabetic macular edema (DME). In this method, a limited number of OCT data is used during the training step and the Active Shape Model is used to produce synthetic OCTs plus delineation of retinal boundaries and location of abnormalities. Statistical comparison of thickness maps showed that synthetic dataset can be used as a statistically acceptable representative of the original dataset (p > 0.05). Visual inspection of the synthesized vessels was also promising. Regarding the texture features of the synthesized datasets, Q-Q plots were used, and even in cases that the points have slightly digressed from the straight line, the p-values of the Kolmogorov–Smirnov test rejected the null hypothesis and showed the same distribution in texture features of the real and the synthetic data. The proposed algorithm provides a unique benchmark for comparison of OCT enhancement methods and a tailored augmentation method to overcome the limited number of OCTs in deep learning algorithms. Graphical abstract: [Figure not available: see fulltext.] © 2021, The Author(s).
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