基于相对距离的补丁分布建模 (R-PaDiM) 的光学连贯性断层扫描血管图像的质量评估
Meltem Esengönül1, Teresa Finisterra Araújo1, Natasa Jeremic1
1Laboratory for Ophthalmic Image Analysis, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria.
Computer methods and programs in biomedicine
|August 20, 2025
概括
一种基于相对距离的贴片分布建模 (R-PaDiM) 的新方法可以准确地评估光学连贯性断层扫描 (OCTA) 图像质量. 这种技术可以识别视网膜微血管图像中的物体,改善系统性疾病研究的生物标志物分析.
科学领域:
- 眼科 眼科
- 医学成像
- 人工智能
背景情况:
- 光学连贯断层扫描血管学 (OCTA) 可视化视网膜微血管结构的变化.
- OCTA生物标志物将视网膜血流与全身性疾病联系起来.
- 在OCTA图像中的工件损害了质量并阻碍了分析.
研究的目的:
- 开发一个强大的图像质量评估 (IQA) 方法,用于OCTA面对面的图像.
- 从OCTA数据提取视网膜生物标志物的可靠性.
- 提高医学成像中的深度学习模型的可解释性.
主要方法:
- 针对OCTA IQA提出的基于相对距离的补丁分布模型 (R-PaDiM).
- 使用预训练编码器的补丁功能进行概率图像表示.
- 将图像分类为好/坏质量,并生成贴片质量图.
- 在DRAC挑战中评估了R-PaDiM,OCTA-25K-IQA-SEG和五个骨干的MeyeHeart数据集.
主要成果:
- 在所有测试的骨干和数据集中实现了最先进的性能.
- 在使用WideResNet-50的DRAC挑战数据集中获得了98. 0%的准确性,99. 3%的AUC和86. 4卡帕.
- 与现有方法相比,显示了补丁智能分数和文物区域之间的优异相关性.
结论:
- R-PaDiM有效地提供与特定图像区域相关的强大和高效的图像质量得分.
- 该方法提高了OCTA分析的深度学习模型的可解释性.
- R-PaDiM的多功能性使其适用于各种成像方式和任务.
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