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免疫组织化学注释增强了AI识别淋巴细胞和中性粒细胞在数字化H&E幻灯片中的炎症性肠病

Natalia Zurek¹, Yi Zhang², Dermot P B McGovern¹

¹F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Computer methods and programs in biomedicine

|September 22, 2024

概括

这项研究开发了一种自动管道,用于在H&E幻灯片中标记免疫细胞,从而创建了NeuLy-IHC数据集. 在这些数据上训练的HoVer-Net (NeuLy) 模型显著提高了识别中性粒细胞和淋巴细胞的准确性,以更好地诊断疾病.

科学领域:

数字病理学数字病理学
计算病理学计算病理学
组织学中的深度学习.

背景情况:

在H&E幻灯片中对免疫透物的组织学评估对于炎症性肠病 (IBD) 的诊断和管理至关重要.
目前的手动评估是主观的,耗时的,并且由于用于训练深度学习模型的注释图像数据稀缺而受到限制.

研究的目的:

开发一个自动化管道,在H&E幻灯片中精确标记中性粒细胞和淋巴细胞.
创建一个新的,大规模的注释数据集 (NeuLy-IHC),用于训练深度学习模型.
训练和评估一个深度学习模型 (HoVer-Net ((NeuLy)) 用于免疫细胞量化.

主要方法:

开发了一种管道,可以自动划分H&EROI中的核,并使用色度特征和图像注册从免疫组织化学 (IHC) 染色ROI转移标签.
创建了NeuLy-IHC数据集,包含519个ROI和235,256个标记细胞 (淋巴细胞,中性粒细胞和其他).
在NeuLy-IHC数据集上训练了HoVer-Net ((NeuLy) 模型,并将其性能 (DICE系数,F1分数) 与现有模型 (HoVer-Net ((MoNuSAC),SMILE ((MoNuSAC)) 进行了比较.

主要成果:

实现了较低的中位数目标注册误差 (1.0μm),表明标签从IHC转移到H&E幻灯片.
HoVer-Net ((NeuLy) 的表现优越,DICE系数为0.861,F1得分为0.827-0.838,表现优于基线模型.
在IBD数据上训练的模型在未见的癌症样本数据上表现强.

结论:

开发的管道和NeuLy-IHC数据集使得在数字组织学中准确量化炎症透物.
HoVer-Net ((NeuLy) 的性能提升归因于NeuLy-IHC数据集的规模更大,更全面.
这种方法有望提高IBD和潜在的其他疾病的诊断准确性和效率.

关键词:

计算病理学计算病理学深度学习是一种深度学习.免疫细胞检测检测免疫细胞检测免疫组织化学免疫组织化学炎症性肠病是一种炎症性肠病.恢复过来的幻灯片.

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