Impact of Data Augmentation on Retinal OCT Image Segmentation for Diabetic Macular Edema Analysis

Bar-David, Daniel; Bar-David, Laura; Shoudry, Shiri; Fischer, Anath

doi:10.1007/978-3-030-87000-3_16

Cited by 3 publications

(3 citation statements)

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“…Our modified GoogLeNet was retrained with initial weight freeze 30 and data augmentation, 19 as both of these techniques have been shown to improve CNN efficiency of image classification. Data augmentation refers to slight alterations of images (rotation and inversion) fed to networks to increase the training sample.…”

Section: Discussionmentioning

confidence: 99%

“…All CNNs were trained and tested with and without initial weight freeze. Further, data augmentation 19 in conjunction with initial weight freeze was used for retraining GoogLeNet. Data augmentation used slightly modified version of images; image rotation (three images with up to 30° rotation) and y-axis reflection of images were fed to the networks for training.…”

Section: Methodsmentioning

confidence: 99%

“…Data augmentation used slightly modified version of images; image rotation (three images with up to 30° rotation) and y-axis reflection of images were fed to the networks for training. 19 The training of all four modified CNNs was performed using MATLAB (MathWorks Inc., Natick, MA, USA) on a single computer (8-core CPU. Windows 10; Microsoft, Redmond, WA, USA) with parallel processing using eight workers.…”

Section: Methodsmentioning

confidence: 99%

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Deep Learning Algorithm Detects Presence of Disorganization of Retinal Inner Layers (DRIL)–An Early Imaging Biomarker in Diabetic Retinopathy

Singh

Singuri

Batoki

et al. 2023

Trans. Vis. Sci. Tech.

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Purpose To develop and train a deep learning-based algorithm for detecting disorganization of retinal inner layers (DRIL) on optical coherence tomography (OCT) to screen a cohort of patients with diabetic retinopathy (DR). Methods In this cross-sectional study, subjects over age 18, with ICD-9/10 diagnoses of type 2 diabetes with and without retinopathy and Cirrus HD-OCT imaging performed between January 2009 to September 2019 were included in this study. After inclusion and exclusion criteria were applied, a final total of 664 patients (5992 B-scans from 1201 eyes) were included for analysis. Five-line horizontal raster scans from Cirrus HD-OCT were obtained from the shared electronic health record. Two trained graders evaluated scans for presence of DRIL. A third physician grader arbitrated any disagreements. Of 5992 B-scans analyzed, 1397 scans (∼30%) demonstrated presence of DRIL. Graded scans were used to label training data for the convolution neural network (CNN) development and training. Results On a single CPU system, the best performing CNN training took ∼35 mins. Labeled data were divided 90:10 for internal training/validation and external testing purpose. With this training, our deep learning network was able to predict the presence of DRIL in new OCT scans with a high accuracy of 88.3%, specificity of 90.0%, sensitivity of 82.9%, and Matthews correlation coefficient of 0.7. Conclusions The present study demonstrates that a deep learning-based OCT classification algorithm can be used for rapid automated identification of DRIL. This developed tool can assist in screening for DRIL in both research and clinical decision-making settings. Translational Relevance A deep learning algorithm can detect disorganization of retinal inner layers in OCT scans.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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