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RegWSI:使用结合深度特征和强度的方法进行整个幻灯片图像的注册:ACROBAT 2023挑战赛的获胜者
Marek Wodzinski1, Niccolò Marini2, Manfredo Atzori3
1Institute of Informatics, University of Applied Sciences Western Switzerland, Sierre, Switzerland; Department of Measurement and Electronics, AGH University of Kraków, Krakow, Poland.
Computer methods and programs in biomedicine
|April 24, 2024
概括
本研究引入了一种新的双步混合方法,用于自动全幻灯片图像 (WSI) 记录,实现数字病理学应用的高精度和效率,无需对数据集进行特定微调.
科学领域:
- 数字病理学数字病理学
- 医学图像分析 医学图像分析
- 计算生物学 计算生物学
背景情况:
- 准确的记录不同染色的整片图像 (WSIs) 对于诊断和预后至关重要.
- 挑战包括幻灯片准备,染色和组织变形的变化.
- 在串行WSIs之间有效的注释传输可以显著减少时间和成本.
研究的目的:
- 开发一个强大的,高效的,准确的WSIs的自动注册方法.
- 为了解决数字病理学当前注册技术的局限性.
- 提供适用于各种组织类型和染色的可通用解决方案.
主要方法:
- 一种两步混合方法,结合了深度学习/基于特征的初始调整和基于强度的非刚性注册与实例优化.
- 该方法不需要微调,直接适用于各种数据集.
- 在DeeperHistReg框架内实现多功能WSI处理.
主要成果:
- 在ACROBAT 2023挑战赛中获得第一名.
- 与ANHIR,ACROBAT和HyReCo数据集上的最先进的方法相比,表现出更高的性能.
- 展示了在细胞级注册的高准确性,并通过不同的分辨率强大的性能.
结论:
- 开发的方法提供了自动,稳固和高度准确的WSI注册.
- 它是微观图像注册的可通用,即时的解决方案.
- 开源版本和可重复的结果提升了数字病理学能力.
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