PDF(Pubmed)
Abstract：
UNASSIGNED: Due to limited imaging conditions, the quality of fundus images is often unsatisfactory, especially for images photographed by handheld fundus cameras. Here, we have developed an automated method based on combining two mirror-symmetric generative adversarial networks (GANs) for image enhancement.
UNASSIGNED: A total of 1047 retinal images were included. The raw images were enhanced by a GAN-based deep enhancer and another methods based on luminosity and contrast adjustment. All raw images and enhanced images were anonymously assessed and classified into 6 levels of quality classification by three experienced ophthalmologists. The quality classification and quality change of images were compared. In addition, image-detailed reading results for the number of dubiously pathological fundi were also compared.
UNASSIGNED: After GAN enhancement, 42.9% of images increased their quality, 37.5% remained stable, and 19.6% decreased. After excluding the images at the highest level (level 0) before enhancement, a large number (75.6%) of images showed an increase in quality classification, and only a minority (9.3%) showed a decrease. The GAN-enhanced method was superior for quality improvement over a luminosity and contrast adjustment method (P＜0.001). In terms of image reading results, the consistency rate fluctuated from 86.6% to 95.6%, and for the specific disease subtypes, both discrepancy number and discrepancy rate were less than 15 and 15%, for two ophthalmologists.
UNASSIGNED: Learning the style of high-quality retinal images based on the proposed deep enhancer may be an effective way to improve the quality of retinal images photographed by handheld fundus cameras.
UNASSIGNED: A total of 1047 retinal images were included. The raw images were enhanced by a GAN-based deep enhancer and another methods based on luminosity and contrast adjustment. All raw images and enhanced images were anonymously assessed and classified into 6 levels of quality classification by three experienced ophthalmologists. The quality classification and quality change of images were compared. In addition, image-detailed reading results for the number of dubiously pathological fundi were also compared.
UNASSIGNED: After GAN enhancement, 42.9% of images increased their quality, 37.5% remained stable, and 19.6% decreased. After excluding the images at the highest level (level 0) before enhancement, a large number (75.6%) of images showed an increase in quality classification, and only a minority (9.3%) showed a decrease. The GAN-enhanced method was superior for quality improvement over a luminosity and contrast adjustment method (P＜0.001). In terms of image reading results, the consistency rate fluctuated from 86.6% to 95.6%, and for the specific disease subtypes, both discrepancy number and discrepancy rate were less than 15 and 15%, for two ophthalmologists.
UNASSIGNED: Learning the style of high-quality retinal images based on the proposed deep enhancer may be an effective way to improve the quality of retinal images photographed by handheld fundus cameras.
摘要：
■由于成像条件有限，眼底图像的质量往往不尽人意，特别是手持眼底照相机拍摄的图像。这里,我们开发了一种基于结合两个镜像对称生成对抗网络（GAN）进行图像增强的自动化方法。
■共包括1047张视网膜图像。通过基于GAN的深度增强器和基于亮度和对比度调整的另一种方法来增强原始图像。所有原始图像和增强图像均由三位经验丰富的眼科医生匿名评估并分类为6个质量分类级别。比较图像的质量分类和质量变化。此外,还比较了可疑病理基础数量的图像详细阅读结果。
■GAN增强后，42.9％的图像提高了质量，37.5%保持稳定,下降19.6%。在排除增强前的最高级别（级别0）的图像后，大量(75.6%)的图像显示质量分类增加，只有少数(9.3%)出现下降。GAN增强方法在质量改善方面优于光度和对比度调整方法（P＜0.001）。在图像读取结果方面，一致性率从86.6%波动到95.6%，对于特定的疾病亚型，差异数和差异率均小于15%和15%，给两位眼科医生.
■学习基于所提出的深度增强器的高质量视网膜图像的风格可能是提高手持式眼底相机拍摄的视网膜图像质量的有效方法。
■共包括1047张视网膜图像。通过基于GAN的深度增强器和基于亮度和对比度调整的另一种方法来增强原始图像。所有原始图像和增强图像均由三位经验丰富的眼科医生匿名评估并分类为6个质量分类级别。比较图像的质量分类和质量变化。此外,还比较了可疑病理基础数量的图像详细阅读结果。
■GAN增强后，42.9％的图像提高了质量，37.5%保持稳定,下降19.6%。在排除增强前的最高级别（级别0）的图像后，大量(75.6%)的图像显示质量分类增加，只有少数(9.3%)出现下降。GAN增强方法在质量改善方面优于光度和对比度调整方法（P＜0.001）。在图像读取结果方面，一致性率从86.6%波动到95.6%，对于特定的疾病亚型，差异数和差异率均小于15%和15%，给两位眼科医生.
■学习基于所提出的深度增强器的高质量视网膜图像的风格可能是提高手持式眼底相机拍摄的视网膜图像质量的有效方法。
