Image enhancement of wide-field retinal optical coherence tomography angiography by super-resolution angiogram reconstruction generative adversarial network

Yuan, Xing; An, L.; Qin, Jia; Lan, Gongpu; Qiu, Haixia; Yu, Bo; Jia, Haibo; Ren, Shangjie; Tan, Haishu; Xu, Jianbin

doi:10.1016/j.bspc.2022.103957

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…Recently, deep learning has brought about significant advancements in the interpretation of OCT images. This progress extends to various tasks, such as retinal layer and fluid segmentation [12][13][14][15], noise removal [16,17], image super-resolution [18,19], image generation [20], and disease classification [21,22]. For instance, in the context of retinal layer and fluid segmentation, researchers in [12] proposed a new convolutional neural architecture, namely RetiFluidNet, for multi-class retinal fluid segmentation.…”

Section: Related Workmentioning

confidence: 99%

Multi-Scale Learning with Sparse Residual Network for Explainable Multi-Disease Diagnosis in OCT Images

Bui,

Le,

Bum

et al. 2023

Bioengineering

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In recent decades, medical imaging techniques have revolutionized the field of disease diagnosis, enabling healthcare professionals to noninvasively observe the internal structures of the human body. Among these techniques, optical coherence tomography (OCT) has emerged as a powerful and versatile tool that allows high-resolution, non-invasive, and real-time imaging of biological tissues. Deep learning algorithms have been successfully employed to detect and classify various retinal diseases in OCT images, enabling early diagnosis and treatment planning. However, existing deep learning algorithms are primarily designed for single-disease diagnosis, which limits their practical application in clinical settings where OCT images often contain symptoms of multiple diseases. In this paper, we propose an effective approach for multi-disease diagnosis in OCT images using a multi-scale learning (MSL) method and a sparse residual network (SRN). Specifically, the MSL method extracts and fuses useful features from images of different sizes to enhance the discriminative capability of a classifier and make the disease predictions interpretable. The SRN is a minimal residual network, where convolutional layers with large kernel sizes are replaced with multiple convolutional layers that have smaller kernel sizes, thereby reducing model complexity while achieving a performance similar to that of existing convolutional neural networks. The proposed multi-scale sparse residual network significantly outperforms existing methods, exhibiting 97.40% accuracy, 95.38% sensitivity, and 98.25% specificity. Experimental results show the potential of our method to improve explainable diagnosis systems for various eye diseases via visual discrimination.

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Section: Related Workmentioning

confidence: 99%

Multi-Scale Learning with Sparse Residual Network for Explainable Multi-Disease Diagnosis in OCT Images

Bui,

Le,

Bum

et al. 2023

Bioengineering

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“…Several studies have utilized deep learning-based methods to enhance functional blood flow images and demonstrated the feasibility and robustness of deep learning for OCTA image processing [31][32][33][34][35]. These enhancement methods have been designed for ophthalmology and neurology, where imaging subjects, that is, retina and mouse brain, are semi-transparent and easy to obtain high-quality image labels with clear vascular visualization for network training.…”

Section: Introductionmentioning

confidence: 99%

Deep‐learning visualization enhancement method for optical coherence tomography angiography in dermatology

Yuan

Qin

et al. 2023

Journal of Biophotonics

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Optical coherence tomography angiography (OCTA) in dermatology usually suffers from low image quality due to the highly scattering property of the skin, the complexity of cutaneous vasculature, and limited acquisition time. Deep‐learning methods have achieved great success in many applications. However, the deep learning approach to improve dermatological OCTA images has not been investigated due to the requirement of high‐performance OCTA systems and difficulty of obtaining high‐quality images as ground truth. This study aims to generate proper datasets and develop a robust deep learning method to enhance the skin OCTA images. A swept‐source skin OCTA system was employed to create low‐quality and high‐quality OCTA images with different scanning protocols. We propose a model named vascular visualization enhancement generative adversarial network and adopt an optimized data augmentation strategy and perceptual content loss function to achieve better image enhancement effect with small amount of training data. We demonstrate the superiority of the proposed method in skin OCTA image enhancement by quantitative and qualitative comparisons.

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“…Deep learning has been applied to OCT [18,19] images, particularly in the field of blood vessel segmentation [20] and vessel image enhancement [21]. Many techniques [22][23][24] based on fully convolutional networks (FCNs) [25][26][27] have been applied to medical image segmentation, among which U-Net [28] has achieved outstanding results in medical image segmentation using the symmetrical U-shaped structure of an encoder and decoder [29].…”

Section: Introductionmentioning

confidence: 99%

One-Shot Learning for Optical Coherence Tomography Angiography Vessel Segmentation Based on Multi-Scale U2-Net

Liu,

Guo,

Cong

et al. 2023

Mathematics

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Vessel segmentation in optical coherence tomography angiography (OCTA) is crucial for the detection and diagnosis of various eye diseases. However, it is hard to distinguish intricate vessel morphology and quantify the density of blood vessels due to the large variety of vessel sizes, significant background noise, and small datasets. To this end, a retinal angiography multi-scale segmentation network, integrated with the inception and squeeze-and-excitation modules, is proposed to address the above challenges under the one-shot learning paradigm. Specifically, the inception module extends the receptive field and extracts multi-scale features effectively to handle diverse vessel sizes. Meanwhile, the squeeze-and-excitation module modifies channel weights adaptively to improve the vessel feature extraction ability in complex noise backgrounds. Furthermore, the one-shot learning paradigm is adapted to alleviate the problem of the limited number of images in existing retinal OCTA vascular datasets. Compared with the classic U2-Net, the proposed model gains improvements in the Dice coefficient, accuracy, precision, recall, and intersection over union by 3.74%, 4.72%, 8.62%, 4.87%, and 4.32% respectively. The experimental results demonstrate that the proposed one-shot learning method is an effective solution for retinal angiography image segmentation.

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Image enhancement of wide-field retinal optical coherence tomography angiography by super-resolution angiogram reconstruction generative adversarial network

Cited by 7 publications

References 41 publications

Multi-Scale Learning with Sparse Residual Network for Explainable Multi-Disease Diagnosis in OCT Images

Multi-Scale Learning with Sparse Residual Network for Explainable Multi-Disease Diagnosis in OCT Images

Deep‐learning visualization enhancement method for optical coherence tomography angiography in dermatology

One-Shot Learning for Optical Coherence Tomography Angiography Vessel Segmentation Based on Multi-Scale U2-Net

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