通过图像对图像翻译来弥合相机域间隙,改善了对玻璃眼的诊断
Shuang He1,2, Sanil Joseph3,4,5, Gabriella Bulloch4
1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
Translational vision science & technology
|December 22, 2023
概括
一个生成对抗网络 (GAN) 模型将Optain图像转换为Topcon图像,通过减少虚假阳性和增强诊断模型的概括性来改进光眼神经病变 (GON) 的自动诊断.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 绿斑视神经病变 (GON) 的自动诊断对于早期发现和治疗至关重要.
- 不同的 fundus 摄像机 (如 Optain 和 Topcon) 之间的图像质量和参数差异可能会阻碍诊断模型的性能.
- 在Optain图像中光盘的过度曝光对准确的GON评估提出了挑战.
研究的目的:
- 改进绿斑视神经病变 (GON) 的自动诊断.
- 开发一个生成对抗网络 (GAN) 模型用于图像对图像的翻译,将Optain fundus图像转换为相当于Topcon相机的图像.
- 增强深度学习诊断模型在不同摄像头系统中的通用性.
主要方法:
- 在725个配对的Topcon和Optain图像上训练了一个GAN模型.
- 外部验证使用来自另一个医院的843张配对图像进行.
- 使用RMSE,PSNR,SSIM,协议极限,皮尔森的相关性和科恩的卡帕等指标来评估性能,将GON模型性能与原始Optain,Topcon和GAN翻译图像进行比较.
主要成果:
- 该GAN模型显著减少了来自Optain图像的GON诊断虚假阳性结果.
- 定量指标显示,在GAN转换后,图像质量和参数一致性得到改善 (例如,VCDR的Pearson相关性从0.61增加到0.85).
- 科恩的卡帕从0.32提高到0.60,表明图像翻译后诊断一致性得到了实质性的增强.
结论:
- 生成对抗网络 (GAN) 可以有效地在不同的 fundus 摄像机类型之间执行图像对图像的翻译.
- 这种方法解决了Optain摄像头中光盘过度曝光的问题,提高了诊断准确度.
- 开发的GAN模型增强了基于深度学习的GON诊断工具的稳定性和通用性.
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