Retinal optical coherence tomography image enhancement via deep learning

Halupka, Kerry; Antony, Bhavna J.; Lee, Mathew; Lucy, Katie A.; Ravneet, S.; Ishikawa, Hiroshi; Wollstein, Gadi; Schuman, Joel S.; Garnavi, Rahil

doi:10.1364/boe.9.006205

Cited by 90 publications

(52 citation statements)

References 37 publications

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“…Advances in the diagnosis and understanding of the DME disease have been made in the recent decades [6], including the proposal of new therapies besides macular laser therapy. Also, retinal image analysis using Optical Coherence Tomography (OCT) scans became popular for the pathological DME identification [7] and characterization using both traditional classification techniques [8] or more novel ones, such as deep learning strategies [9,10]. Research in these scenarios contributes to the improvement of diagnosis, prognosis and monitoring tasks of the DME disease.…”

Section: Introductionmentioning

confidence: 99%

Automatic identification and characterization of the epiretinal membrane in OCT images

Baamonde

Moura

Novo

et al. 2019

Biomed. Opt. Express

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Optical coherence tomography (OCT) is a medical image modality that is used to capture, non-invasively, high-resolution cross-sectional images of the retinal tissue. These images constitute a suitable scenario for the diagnosis of relevant eye diseases like the vitreomacular traction or the diabetic retinopathy. The identification of the epiretinal membrane (ERM) is a relevant issue as its presence constitutes a symptom of diseases like the macular edema, deteriorating the vision quality of the patients. This work presents an automatic methodology for the identification of the ERM presence in OCT scans. Initially, a complete and heterogeneous set of features was defined to capture the properties of the ERM in the OCT scans. Selected features went through a feature selection process to further improve the method efficiency. Additionally, representative classifiers were trained and tested to measure the suitability of the proposed approach. The method was tested with a dataset of 285 OCT scans labeled by a specialist. In particular, 3,600 samples were equally extracted from the dataset, representing zones with and without ERM presence. Different experiments were conducted to reach the most suitable approach. Finally, selected classifiers were trained and compared using different metrics, providing in the best configuration an accuracy of 89.35%.

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Section: Introductionmentioning

confidence: 99%

Automatic identification and characterization of the epiretinal membrane in OCT images

Baamonde

Moura

Novo

et al. 2019

Biomed. Opt. Express

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“…Shortly, the GAN network contains a generative network to produce an image and a discriminator network to estimate the quality of the image produced by the generator. A variation of this architecture, called Wasserstein generative adversarial network (WGAN), which uses Wasserstein distance as a loss function, was recently utilized for resolution enhancement of OCT images . An alternative, edge‐sensitive conditional generative adversarial network (cGAN) was reported efficient against speckle noise.…”

Section: Deep Learning For Biophotonic Imagingmentioning

confidence: 99%

Deep learning a boon for biophotonics?

Pradhan

Guo

Ryabchykov

et al. 2020

Journal of Biophotonics

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This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.

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“…DL based algorithms have recently surpassed the performance of conventional methods in noise reduction tasks [27][28][29][30][31][32][33][34], and the applications of DL based algorithm to retinal layer segmentation have been reported [35][36][37][38][39]. Recent studies utilizing DL models for OCT image noise reduction have shown promising results [40,41]. However, to train the deep learning models, they all rely on paired images where one image is noisy and the other one is noise-reduced.…”

Section: Enhanced Oct Image Visualization Through Deep Learning Basedmentioning

confidence: 99%

“…In previous studies, n ranges from 6 to 60 [40,41]. With the current commercially available high-speed swept-source OCT devices such as the Triton OCT device that is operated at 100 kHz/sec, it is able to capture and average 128 repeated scans at a single location.…”

Section: Enhanced Oct Image Visualization Through Deep Learning Basedmentioning

confidence: 99%

Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans

Mao¹,

Miki

Mei³

et al. 2019

Biomed. Opt. Express

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A deep-learning (DL) based noise reduction algorithm, in combination with a vessel shadow compensation method and a three-dimensional (3D) segmentation technique, has been developed to achieve, to the authors best knowledge, the first automatic segmentation of the anterior surface of the lamina cribrosa (LC) in volumetric ophthalmic optical coherence tomography (OCT) scans. The present DL-based OCT noise reduction algorithm was trained without the need of noise-free ground truth images by utilizing the latest development in deep learning of de-noising from single noisy images, and was demonstrated to be able to cover more locations in the retina and disease cases of different types to achieve high robustness. Compared with the original single OCT images, a 6.6 dB improvement in peak signal-to-noise ratio and a 0.65 improvement in the structural similarity index were achieved. The vessel shadow compensation method analyzes the energy profile in each A-line and automatically compensates the pixel intensity of locations underneath the detected blood vessel. Combining the noise reduction algorithm and the shadow compensation and contrast enhancement technique, medical experts were able to identify the anterior surface of the LC in 98.3% of the OCT images. The 3D segmentation algorithm employs a two-round procedure based on gradients information and information from neighboring images. An accuracy of 90.6% was achieved in a validation study involving 180 individual B-scans from 36 subjects, compared to 64.4% in raw images. This imaging and analysis strategy enables the first automatic complete view of the anterior LC surface, to the authors best knowledge, which may have the potentials in new LC parameters development for glaucoma diagnosis and management.

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Retinal optical coherence tomography image enhancement via deep learning

Cited by 90 publications

References 37 publications

Automatic identification and characterization of the epiretinal membrane in OCT images

Automatic identification and characterization of the epiretinal membrane in OCT images

Deep learning a boon for biophotonics?

Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans

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