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快速和缓慢的线粒分裂检测:对线粒细胞数字的强大和高效的检测

Mostafa Jahanifar¹, Adam Shephard¹, Neda Zamanitajeddin¹

¹Tissue Image Analytic (TIA) Center, Department of Computer Science, University of Warwick, UK.

Medical image analysis

|March 5, 2024

概括

在癌症中手动计数线粒分裂是困难的,容易出现错误. 这项研究引入了两阶段的深度学习框架,用于准确的自动线粒分裂检测,改善癌症分级和预后.

科学领域:

病理学病理学病理学
计算机科学计算机科学
医疗成像医学成像

背景情况:

手动化计数对于癌症分级和预后至关重要,但很费力和主观.
现有的自动转移检测方法在组织学图像中与域移动作斗争.

研究的目的:

开发一个强大而高效的自动化线粒分裂检测框架.
提高癌症组织学中线粒分裂计数的准确性和概括性.

主要方法:

一个两阶段的深度学习框架:EUNet用于快速候选人细分和EfficientNet-B7用于候选人改进.
纳入域泛化技术,以提高对域移动的稳定性.
在三个大型公开的线粒细胞分裂数据集和TCGA乳腺癌队列上进行验证.

主要成果:

拟议的框架实现了最先进的性能和通用性.
该模型赢得了MIDOG21和MIDOG22的线粒分裂域概括挑战.
从TCGA乳腺癌图像中生成了一个超过620,000个潜在的线粒体数字的存储库.

结论:

开发的两阶段框架提供了一个强大的,高效的解决方案,用于自动检测线粒分裂.
这种方法有可能在癌症诊断和研究中显著帮助病理学家.
生成的数据集为计算病理学的未来研究提供了宝贵的资源.

关键词:

乳腺癌乳腺癌乳腺癌计算病理学计算病理学深度学习是一种深度学习.检测检测检测检测检测这就是MIDOGOG.线粒分裂 (mitosis) 是一种发生在细胞的过程.分段化分段化分段化分段化图帕克公司 (TUPAC) 是一个.

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