2022
DOI: 10.1186/s12886-022-02299-w
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Development and quantitative assessment of deep learning-based image enhancement for optical coherence tomography

Abstract: Purpose To develop a deep learning-based framework to improve the image quality of optical coherence tomography (OCT) and evaluate its image enhancement effect with the traditional image averaging method from a clinical perspective. Methods 359 normal eyes and 456 eyes with various retinal conditions were included. A deep learning framework with high-resolution representation was developed to achieve image quality enhancement for OCT images. The qu… Show more

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Cited by 4 publications
(3 citation statements)
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“…Consequently, satisfactory PSNR and SSIM values were measured (34.59 ± 5.34 and 0.88 ± 0.08, respectively), which were similar to or slightly better than those yielded by the deep learning algorithm developed by other authors (Table 1). 3,[22][23][24] The SSIM is a measurement indicator for the human visual system that considers factors such as luminance, contrast, and structure instead of simple objective differences between images. 18,19 In other words, an SSIM value of approximately 1 implies that humans, particularly vitreoretinal surgeons, do not perceive any difference between manually adjusted and optimized surgical images.…”
Section: Discussionmentioning
confidence: 99%
“…Consequently, satisfactory PSNR and SSIM values were measured (34.59 ± 5.34 and 0.88 ± 0.08, respectively), which were similar to or slightly better than those yielded by the deep learning algorithm developed by other authors (Table 1). 3,[22][23][24] The SSIM is a measurement indicator for the human visual system that considers factors such as luminance, contrast, and structure instead of simple objective differences between images. 18,19 In other words, an SSIM value of approximately 1 implies that humans, particularly vitreoretinal surgeons, do not perceive any difference between manually adjusted and optimized surgical images.…”
Section: Discussionmentioning
confidence: 99%
“…To establish that the synthetic images are not just copies of training images, comparison of a subset of synthetic images to training images by the structural similarity index measure (SSIM) in a pairwise manner was conducted ( 26 , 27 ). SSIM is a perceptual metric that measures the perceptual difference between two images based on luminance, contrast, and structure.…”
Section: Methodsmentioning
confidence: 99%
“…In many areas of artificial intelligence, machine learning algorithms based on big data (Wang et al [1] and Ching-Yu et al [2]) and deep learning have facilitated extraordinary progress, with many applications in ophthalmology (Keenan et al [3], Papadopoulos et al [4], Rampasek et al [5], Ishii et al [6], Kermany et al [7], Liu Y. et al [8], Liu T. et al [9], Burlina et al [10], Cen et al [11], Zheng et al [12], Shekar et al [13], and by Zhao et al [14]).…”
Section: Introductionmentioning
confidence: 99%