Synthetic OCT data in challenging conditions: three-dimensional OCT and presence of abnormalities

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

Synthetic OCT Data Generation to Enhance the Performance of Diagnostic Models for Neurodegenerative Diseases

“…Figure 3 shows examples of the synthesized layer boundaries for each of the classes (HC, MS, and NMO). As we focus on retinal thickness maps because of their usefulness as biomarkers for neurologic diseases, it is not necessary to create synthetic textures in order to generate a full OCT image, as we did in Danesh et al 24 …”

“…The construction of synthetic OCT data has been considered by researchers over the past few years. 19 – 22 In recent work, 23 , 24 we used an active shape model (ASM 25 ) to construct synthetic two-dimensional (2D) and three-dimensional (3D) OCT data in the macular region. In this article, we use that model as an augmentation method to generate synthetic 3D OCT boundaries of the macular region from healthy controls (HCs) and patients with MS and NMO.…”

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“…Optical coherence tomography (OCT), as a non-invasive optical imaging technique, has been rapidly developed during the last decades 6 . OCT has strong tissue penetration, contains stereoscopic imaging, and provides more details of the lesions, thereby supporting the quantification of the lesions directly 7 . Based on OCT images, researchers have proposed a number of predictors of MH surgical efficacy 8 .…”

A Clinician's Guide to Sharing Data for AI in Ophthalmology

“…This synthetic data aids in training vessel segmentation algorithms without the need for manual annotation. Danesh et al 33 proposed a method that used the Active Shape Model with a limited number of OCT images for training to create synthetic OCTs, delineate retinal boundaries, and point to abnormalities. The comparison of thickness maps showed that the synthetic dataset can be used as a statistically acceptable representative of the original dataset, suggesting that the proposed algorithm provided an augmentation method for OCT scans.…”