科尼克挑战:推动核检测,细分,分类和计数的边界
Simon Graham1, Quoc Dang Vu1, Mostafa Jahanifar2
1Tissue Image Analytics Centre, University of Warwick, Coventry, United Kingdom; Histofy Ltd, Birmingham, United Kingdom.
Medical image analysis
|December 29, 2023
概括
社区挑战 CoNIC 在结肠组织学中进行了先进的核细分和细胞组成分析. 改进的算法提高了癌症结果的预测,并揭示了免疫细胞在瘤中的作用.
科学领域:
- 计算病理学计算病理学
- 数字病理学数字病理学
- 生物医学图像分析
背景情况:
- 准确的核检测,细分和形态分析对理解组织学-患者结果关系至关重要.
- 这一领域的创新需要用于细胞分析的强大,可重现的算法.
研究的目的:
- 通过社区范围的挑战,推动核细分和细胞组成分析方面的创新.
- 开发和评估可复制的细胞识别算法,使用大量结肠组织图像数据集.
主要方法:
- 建立了结肠核识别挑战 (CoNIC) 使用最大的核细分和细胞组成数据集.
- 通过使用高性能模型对1658张全幻灯片图像进行了挑战后分析,每种模型分析了大约7亿个核.
- 利用检测到的细胞核特征来分类发育不良和进行生存分析.
主要成果:
- CoNIC挑战刺激了可重复算法的开发,显著改善了核细分的最新技术.
- 由于挑战驱动的算法改进,形症分级和生存分析的下游表现显示出显著的提升.
- 分析突出了eosinophils和中性粒细胞在瘤微环境中的重要作用.
结论:
- 在CoNIC挑战中,成功地在计算病理学中进行了先进的核细分和细胞组成分析.
- 从挑战中获得的改进算法增强了对患者结果和生物标志物发现的预测能力.
- 发布的挑战模型和结果将促进数字病理学和癌症研究的进一步研究.
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