其他:最佳运输引导未配对图像对图像翻译通过增强满足规范化的地方
Wenhui Zhu1, Peijie Qiu2, Oana M Dumitrascu3
1School of Computing and Augmented Intelligence, Arizona State Univ., AZ, USA.
Information processing in medical imaging : proceedings of the ... conference
|July 10, 2023
概括
这项研究引入了最佳传输 (OT) 功能,用于增强低质量的视网膜图像,提高诊断准确度. 新的OTRE框架显著提高了糖尿病视网膜病变分级和病变细分任务的性能.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 非肌性视网膜色底摄影 (CFP) 对于眼睛疾病诊断至关重要,但由于各种因素,图像质量往往不佳.
- 高质量的视网膜图像对于准确的医学诊断和自动化分析至关重要,在临床实践中构成重大挑战.
研究的目的:
- 使用最佳运输 (OT) 理论开发无监督的图像到图像翻译方案,以增强低质量的视网膜CFP.
- 将基于OT的增强与基于模型的图像重建方法 (通过无声化进行规范化) 集成,以创建一个名为OTRE的强大框架.
- 验证OTRE框架对公共数据集的有效性,以提高图像质量和下游诊断任务.
主要方法:
- 提出了一种基于最佳传输 (OT) 理论的未配对图像到图像翻译方法,以将低质量的视网膜图像映射到高质量的视网膜图像.
- 开发了一个通用的图像重建方法,通过增强 (RE) 规范化,通过将OT引导网络的先验纳入.
- 将基于OT的增强与RE集成到OTRE框架中,并在三个公开的视网膜图像数据集上验证了它.
主要成果:
- OTRE框架显著提高了视网膜底部图像的质量.
- 来自OTRE的增强图像在下游任务中表现出卓越的性能,包括糖尿病视网膜病变分类和血管/损伤细分.
- 拟议的OTRE框架在实验评估中超越了最先进的无监督和监督方法.
结论:
- 该OTRE框架有效地增强低质量的视网膜图像使用最佳运输理论和规范化通过增强.
- 这种方法为改善临床环境中的视网膜图像质量提供了灵活而强大的解决方案.
- 增强的图像质量转化为在关键的眼科诊断和分析任务中提高性能.
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