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Lv H. Speckle attenuation for optical coherence tomography images using the generalized low rank approximations of matrices. OPTICS EXPRESS 2023; 31:11745-11759. [PMID: 37155802 DOI: 10.1364/oe.485097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
A frequently used technology in medical diagnosis is optical coherence tomography (OCT). However, coherent noise, also known as speckle noise, has the potential to severely reduce the quality of OCT images, which would be detrimental to the use of OCT images for disease diagnosis. In this paper, a despeckling method is proposed to effectively reduce the speckle noise in OCT images using the generalized low rank approximations of matrices (GLRAM). Specifically, the Manhattan distance (MD)-based block matching method is first used to find nonlocal similar blocks for the reference one. The left and right projection matrices shared by these image blocks are then found using the GLRAM approach, and an adaptive method based on asymptotic matrix reconstruction is proposed to determine how many eigenvectors are present in the left and right projection matrices. Finally, all the reconstructed image blocks are aggregated to create the despeckled OCT image. In addition, an edge-guided adaptive back-projection strategy is used to improve the despeckling performance of the proposed method. Experiments with synthetic and real OCT images show that the presented method performs well in both objective measurements and visual evaluation.
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2
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Gende M, de Moura J, Novo J, Penedo MG, Ortega M. A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets. Med Biol Eng Comput 2023; 61:1093-1112. [PMID: 36680707 PMCID: PMC10083164 DOI: 10.1007/s11517-022-02742-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 12/09/2022] [Indexed: 01/22/2023]
Abstract
In optical coherence tomography (OCT), there is a trade-off between the scanning time and image quality, leading to a scarcity of high quality data. OCT platforms provide different scanning presets, producing visually distinct images, limiting their compatibility. In this work, a fully automatic methodology for the unpaired visual conversion of the two most prevalent scanning presets is proposed. Using contrastive unpaired translation generative adversarial architectures, low quality images acquired with the faster Macular Cube preset can be converted to the visual style of high visibility Seven Lines scans and vice-versa. This modifies the visual appearance of the OCT images generated by each preset while preserving natural tissue structure. The quality of original and synthetic generated images was compared using BRISQUE. The synthetic generated images achieved very similar scores to original images of their target preset. The generative models were validated in automatic and expert separability tests. These models demonstrated they were able to replicate the genuine look of the original images. This methodology has the potential to create multi-preset datasets with which to train robust computer-aided diagnosis systems by exposing them to the visual features of different presets they may encounter in real clinical scenarios without having to obtain additional data. Graphical Abstract Unpaired mutual conversion between scanning presets. Two generative adversarial models are trained for the conversion of OCT images into images of another scanning preset, replicating the visual features that characterise said preset.
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Affiliation(s)
- Mateo Gende
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, A Coruña, Spain.,Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, A Coruña, 15071, A Coruña, Spain
| | - Joaquim de Moura
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, A Coruña, Spain. .,Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, A Coruña, 15071, A Coruña, Spain.
| | - Jorge Novo
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, A Coruña, Spain.,Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, A Coruña, 15071, A Coruña, Spain
| | - Manuel G Penedo
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, A Coruña, Spain.,Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, A Coruña, 15071, A Coruña, Spain
| | - Marcos Ortega
- Grupo, VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, A Coruña, Spain.,Centro de investigación, CITIC, Universidade da Coruña, Campus de Elviña s/n, A Coruña, 15071, A Coruña, Spain
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3
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Bian H, Wang J, Hong C, Liu L, Ji R, Cao S, Abdalla AN, Chen X. GPU-accelerated image registration algorithm in ophthalmic optical coherence tomography. BIOMEDICAL OPTICS EXPRESS 2023; 14:194-207. [PMID: 36698653 PMCID: PMC9841998 DOI: 10.1364/boe.479343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/28/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
Limited to the power of the light source in ophthalmic optical coherence tomography (OCT), the signal-to-noise ratio (SNR) of the reconstructed images is usually lower than OCT used in other fields. As a result, improvement of the SNR is required. The traditional method is averaging several images at the same lateral position. However, the image registration average costs too much time, which limits its real-time imaging application. In response to this problem, graphics processing unit (GPU)-side kernel functions are applied to accelerate the reconstruction of the OCT signals in this paper. The SNR of the images reconstructed from different numbers of A-scans and B-scans were compared. The results demonstrated that: 1) There is no need to realize the axial registration with every A-scan. The number of the A-scans used to realize axial registration is suitable to set as ∼25, when the A-line speed was set as ∼12.5kHz. 2) On the basis of ensuring the quality of the reconstructed images, the GPU can achieve 43× speedup compared with CPU.
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Affiliation(s)
- Haiyi Bian
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, 223003, China
| | - Jingtao Wang
- School of Electronic and Information Engineering, Soochow University, 215006, Suzhou, China
| | - Chengjian Hong
- School of Electronic and Information Engineering, Soochow University, 215006, Suzhou, China
| | - Lei Liu
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, 223003, China
| | - Rendong Ji
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, 223003, China
| | - Suqun Cao
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, 223003, China
| | - Ahmed N. Abdalla
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, 223003, China
| | - Xinjian Chen
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, 223003, China
- School of Electronic and Information Engineering, Soochow University, 215006, Suzhou, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, 215123, Suzhou, China
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4
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Noise Reduction of OCT Images Based on the External Patch Prior Guided Internal Clustering and Morphological Analysis. PHOTONICS 2022. [DOI: 10.3390/photonics9080543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Optical coherence tomography (OCT) is widely used in biomedical imaging. However, noise severely affects diagnosing and identifying diseased tissues on OCT images. Here, a noise reduction method based on the external patch prior guided internal clustering and morphological analysis (E2PGICMA) is developed to remove the noise of OCT images. The external patch prior guided internal clustering algorithm is used to reduce speckle noise. The morphological analysis algorithm is employed to the background for contrast enhancement. OCT images of in vivo normal skin tissues were analyzed to remove noise using the proposed method. The estimated standard deviations of the noise were chosen as different values for evaluating the quantitative metrics. The visual quality improvement includes more textures and fine detail preservation. The denoising effects of different methods were compared. Then, quantitative and qualitative evaluations of this proposed method were conducted. The results demonstrated that the SNR, PSNR, and XCOR were higher than those of the other noise-reduction methods, reaching 15.05 dB, 27.48 dB, and 0.9959, respectively. Furthermore, the presented method’s noise reduction ratio (NRR) reached 0.8999. This proposed method can efficiently remove the background and speckle noise. Improving the proposed noise reduction method would outperform existing state-of-the-art OCT despeckling methods.
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5
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Lu Q, Liu C, Feng W, Xiao Q, Wang X. Deep learning optical image denoising research based on principal component estimation. APPLIED OPTICS 2022; 61:4412-4420. [PMID: 36256279 DOI: 10.1364/ao.455849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/26/2022] [Indexed: 06/16/2023]
Abstract
High-quality denoising of optical interference images usually requires preliminary prediction of the noise level. Although blind denoising can filter the image at the pixel level without noise prediction, it inevitably loses a significant amount of phase information. This paper proposes a fast and high-quality denoising algorithm for optical interference images that combines the merits of a principal component analysis (PCA) and residual neural networks. The PCA is used to analyze the image noise and, in turn, establishes an accurate mapping between the estimated and true noise levels. The mapping helps to select a suitable residual neural network model for image processing, which maximizes the retention of image information and reduces the effect of noise. In addition, a comprehensive evaluation factor to account for the time complexity and denoising effect of the algorithm is proposed, since time complexity can be a dominant concern in some cases of actual measurement. The performance of the denoising algorithm and the effectiveness of the evaluation criterion are demonstrated to be high by processing a set of optical interference images and benchmarking with other denoising algorithms. The proposed algorithm outperforms the previously reported counterparts in a specific area of optical interference image preprocessing and provides an alternative paradigm for other denoising problems of optics, such as holograms and structured light measurements.
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Smitha A, Febin I, Jidesh P. A retinex based non-local total generalized variation framework for OCT image restoration. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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7
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Liu L, Zhai Z, Zhang T, Fan L. Reducing speckle in anterior segment optical coherence tomography images based on a convolutional neural network. APPLIED OPTICS 2021; 60:10964-10974. [PMID: 35200859 DOI: 10.1364/ao.442678] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/15/2021] [Indexed: 06/14/2023]
Abstract
Speckle noise is ubiquitous in the optical coherence tomography (OCT) image of the anterior segment, which greatly affects the image quality and destroys the relevant structural information. In order to reduce the influence of speckle noise in OCT images, a denoising algorithm based on a convolutional neural network is proposed in this paper. Unlike traditional algorithms that directly obtain denoised images, the algorithm model proposed in this paper learns the speckle noise distribution through the constructed trainable OCT dataset and indirectly obtains the denoised result image. In order to verify the performance of the model, we compare the denoising results of the algorithm proposed in this paper with several state-of-the-art algorithms from three perspectives: qualitative evaluation from the subjective visual perspective, quantitative evaluation from objective parameter indicators, and running time. The experimental results show that the proposed algorithm has a good denoising effect on different OCT images of the anterior segment and has good generalization ability. Besides, it retains the relevant details and texture information in the image, and it has strong edge preserving ability. The image of OCT speckle removal can be obtained within 0.4 s, which meets the time limit requirement of clinical application.
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Qiu B, Zeng S, Meng X, Jiang Z, You Y, Geng M, Li Z, Hu Y, Huang Z, Zhou C, Ren Q, Lu Y. Comparative study of deep neural networks with unsupervised Noise2Noise strategy for noise reduction of optical coherence tomography images. JOURNAL OF BIOPHOTONICS 2021; 14:e202100151. [PMID: 34383390 DOI: 10.1002/jbio.202100151] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 08/09/2021] [Accepted: 08/09/2021] [Indexed: 06/13/2023]
Abstract
As a powerful diagnostic tool, optical coherence tomography (OCT) has been widely used in various clinical setting. However, OCT images are susceptible to inherent speckle noise that may contaminate subtle structure information, due to low-coherence interferometric imaging procedure. Many supervised learning-based models have achieved impressive performance in reducing speckle noise of OCT images trained with a large number of noisy-clean paired OCT images, which are not commonly feasible in clinical practice. In this article, we conducted a comparative study to investigate the denoising performance of OCT images over different deep neural networks through an unsupervised Noise2Noise (N2N) strategy, which only trained with noisy OCT samples. Four representative network architectures including U-shaped model, multi-information stream model, straight-information stream model and GAN-based model were investigated on an OCT image dataset acquired from healthy human eyes. The results demonstrated all four unsupervised N2N models offered denoised OCT images with a performance comparable with that of supervised learning models, illustrating the effectiveness of unsupervised N2N models in denoising OCT images. Furthermore, U-shaped models and GAN-based models using UNet network as generator are two preferred and suitable architectures for reducing speckle noise of OCT images and preserving fine structure information of retinal layers under unsupervised N2N circumstances.
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Affiliation(s)
- Bin Qiu
- Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China
- Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Shuang Zeng
- Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China
| | - Xiangxi Meng
- Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
| | - Zhe Jiang
- Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China
- Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Yunfei You
- Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China
- Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Mufeng Geng
- Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China
- Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Ziyuan Li
- Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China
- Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Yicheng Hu
- Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China
- Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Zhiyu Huang
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Chuanqing Zhou
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Qiushi Ren
- Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China
- Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Yanye Lu
- Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
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Fan F, Zhang J, Zhu L, Ma Z, Zhu J. Improving cerebral microvascular image quality of optical coherence tomography angiography with deep learning-based segmentation. JOURNAL OF BIOPHOTONICS 2021; 14:e202100171. [PMID: 34382744 DOI: 10.1002/jbio.202100171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/07/2021] [Accepted: 08/09/2021] [Indexed: 06/13/2023]
Abstract
Optical coherence tomography angiography (OCTA) can map the microvascular networks of the cerebral cortices with micrometer resolution and millimeter penetration. However, the high scattering of the skull and the strong noise in the deep imaging region will distort the vasculature projections and decrease the OCTA image quality. Here, we proposed a deep learning-based segmentation method based on a U-Net convolutional neural network to extract the cortical region from the OCT image. The vascular networks were then visualized by three OCTA algorithms. The image quality of the vasculature projections was assessed by two metrics, including the peak signal-to-noise ratio (PSNR) and the contrast-to-noise ratio (CNR). The results show the accuracy of the cortical segmentation was 96.07%. The PSNR and CNR values increased significantly in the projections of the selected cortical regions. The OCTA incorporating the deep learning-based cortical segmentation can efficiently improve the image quality and enhance the vasculature clarity.
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Affiliation(s)
- Fan Fan
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
- Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
| | - Jisheng Zhang
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
- Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
| | - Lianqing Zhu
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
- Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
| | - Zongqing Ma
- Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
| | - Jiang Zhu
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
- Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
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Shen Z, Xi M, Tang C, Xu M, Lei Z. Double-path parallel convolutional neural network for removing speckle noise in different types of OCT images. APPLIED OPTICS 2021; 60:4345-4355. [PMID: 34143124 DOI: 10.1364/ao.419871] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/30/2021] [Indexed: 06/12/2023]
Abstract
Speckle noises widely exist in optical coherence tomography (OCT) images. We propose an improved double-path parallel convolutional neural network (called DPNet) to reduce speckles. We increase the network width to replace the network depth to extract deeper information from the original OCT images. In addition, we use dilated convolution and residual learning to increase the learning ability of our DPNet. We use 100 pairs of human retinal OCT images as the training dataset. Then we test the DPNet model for denoising speckles on four different types of OCT images, mainly including human retinal OCT images, skin OCT images, colon crypt OCT images, and quail embryo OCT images. We compare the DPNet model with the adaptive complex diffusion method, the curvelet shrinkage method, the shearlet-based total variation method, and the OCTNet method. We qualitatively and quantitatively evaluate these methods in terms of image smoothness, structural information protection, and edge clarity. Our experimental results prove the performance of the DPNet model, and it allows us to batch and quickly process different types of poor-quality OCT images without any parameter fine-tuning under a time-constrained situation.
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Cheong H, Krishna Devalla S, Chuangsuwanich T, Tun TA, Wang X, Aung T, Schmetterer L, Buist ML, Boote C, Thiéry AH, Girard MJA. OCT-GAN: single step shadow and noise removal from optical coherence tomography images of the human optic nerve head. BIOMEDICAL OPTICS EXPRESS 2021; 12:1482-1498. [PMID: 33796367 PMCID: PMC7984803 DOI: 10.1364/boe.412156] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 01/12/2021] [Accepted: 01/15/2021] [Indexed: 06/12/2023]
Abstract
Speckle noise and retinal shadows within OCT B-scans occlude important edges, fine textures and deep tissues, preventing accurate and robust diagnosis by algorithms and clinicians. We developed a single process that successfully removed both noise and retinal shadows from unseen single-frame B-scans within 10.4ms. Mean average gradient magnitude (AGM) for the proposed algorithm was 57.2% higher than current state-of-the-art, while mean peak signal to noise ratio (PSNR), contrast to noise ratio (CNR), and structural similarity index metric (SSIM) increased by 11.1%, 154% and 187% respectively compared to single-frame B-scans. Mean intralayer contrast (ILC) improvement for the retinal nerve fiber layer (RNFL), photoreceptor layer (PR) and retinal pigment epithelium (RPE) layers decreased from 0.362 ± 0.133 to 0.142 ± 0.102, 0.449 ± 0.116 to 0.0904 ± 0.0769, 0.381 ± 0.100 to 0.0590 ± 0.0451 respectively. The proposed algorithm reduces the necessity for long image acquisition times, minimizes expensive hardware requirements and reduces motion artifacts in OCT images.
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Affiliation(s)
- Haris Cheong
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Sripad Krishna Devalla
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Thanadet Chuangsuwanich
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Tin A. Tun
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Xiaofei Wang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Tin Aung
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- School of Clinical and Biomedical Engineering, Nanyang Technological University, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore
- Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria
- Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Martin L. Buist
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Craig Boote
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Structural Biophysics Group, School of Optometry and Vision Sciences, Cardiff University, UK
| | - Alexandre H. Thiéry
- Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Michaël J. A. Girard
- Ophthalmic Engineering and Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Medical School, Singapore
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