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SVPath:一个深度学习工具,用于分析从组织学幻灯片的血管

Aseem Jain¹, Dianela Perdomo², Nimesh Nagururu²

¹College of Medicine, University of Cincinnati, 231 Albert Sabin Way, Cincinnati, OH, 45267, USA. jain2ae@mail.uc.edu.

Journal of the Association for Research in Otolaryngology : JARO

|May 17, 2024

概括

一个新的深度学习工具SVPath从骨幻灯片中准确分析了血管 (SV) 和其毛细血管. 这种自动化的方法有助于通过量化SV缩来研究耳部病理.

科学领域:

眼科病理学眼科病理学
组织病理学组织病理学
医学成像分析医学成像分析

背景情况:

血管 (SV) 在各种耳部疾病中起着至关重要的作用.
目前从骨幻灯片 (TBS) 进行SV缩的手动分析是劳动密集型的.
为了高效的研究,需要对SV特征进行自动化分析.

研究的目的:

开发和验证一个深度学习工具SVPath,用于自动分析TBS的血管及其毛细血管床.
为了使SV形态的定量测量,包括宽度,面积和细胞数量.

主要方法:

利用了203个数字化血素和素染色的耳部份的数据集.
采用深度学习模型YOLOv8用于检测和nnUnet用于细分SV特征.
集成了用于定量形态分析的 SV 分析工具 (SVAT).

主要成果:

YOLOv8在尾管和SV检测方面实现了>90%的准确性.
nnUnet在SV细分 (DICE 0.84-0.95) 和毛细血管床细分 (DICE 0.75-0.88) 中表现出很高的性能.
在 SV 宽度或耳之间的面积上没有发现显著差异,但每 SV 的细胞数量在统计上有所不同.

结论:

该SVPath工具提供精确和高效的分析的血管从骨组织病理学幻灯片.
这种自动化平台有助于进一步研究状血管在耳部疾病中的功能.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.血管 (Stria vascularis) 是一个血管.骨组织学骨组织学

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