Remedios Lucas W, Bao Shunxing, Remedios Samuel W, Lee Ho Hin, Cai Leon Y, Li Thomas, Deng Ruining, Newlin Nancy R, Saunders Adam M, Cui Can, Li Jia, Liu Qi, Lau Ken S, Roland Joseph T, Washington Mary K, Coburn Lori A, Wilson Keith T, Huo Yuankai, Landman Bennett A
Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States.
Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States.
J Med Imaging (Bellingham). 2024 Nov;11(6):067501. doi: 10.1117/1.JMI.11.6.067501. Epub 2024 Nov 5.
Cells are building blocks for human physiology; consequently, understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions in both health and disease. Hematoxylin and eosin (H&E) is the standard stain used in histological analysis of tissues in both clinical and research settings. Although H&E is ubiquitous and reveals tissue microanatomy, the classification and mapping of cell subtypes often require the use of specialized stains. The recent CoNIC Challenge focused on artificial intelligence classification of six types of cells on colon H&E but was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). We propose to use inter-modality learning to label previously un-labelable cell types on H&E.
We took advantage of the cell classification information inherent in multiplexed immunofluorescence (MxIF) histology to create cell-level annotations for 14 subclasses. Then, we performed style transfer on the MxIF to synthesize realistic virtual H&E. We assessed the efficacy of a supervised learning scheme using the virtual H&E and 14 subclass labels. We evaluated our model on virtual H&E and real H&E.
On virtual H&E, we were able to classify helper T cells and epithelial progenitors with positive predictive values of (prevalence ) and (prevalence ), respectively, when using ground truth centroid information. On real H&E, we needed to compute bounded metrics instead of direct metrics because our fine-grained virtual H&E predicted classes had to be matched to the closest available parent classes in the coarser labels from the real H&E dataset. For the real H&E, we could classify bounded metrics for the helper T cells and epithelial progenitors with upper bound positive predictive values of (parent class prevalence 0.21) and (parent class prevalence 0.49) when using ground truth centroid information.
This is the first work to provide cell type classification for helper T and epithelial progenitor nuclei on H&E.
细胞是人体生理学的基本组成部分；因此，了解细胞之间如何通信、共定位和相互关联对于加深我们对人体在健康和疾病状态下功能的理解至关重要。苏木精和伊红（H&E）是临床和研究中用于组织学分析的标准染色方法。尽管H&E无处不在且能揭示组织微观解剖结构，但细胞亚型的分类和映射通常需要使用专门的染色方法。最近的CoNIC挑战赛专注于对结肠H&E上六种细胞类型进行人工智能分类，但无法对上皮亚型（祖细胞、肠内分泌细胞、杯状细胞）、淋巴细胞亚型（B细胞、辅助性T细胞、细胞毒性T细胞）和结缔组织亚型（成纤维细胞）进行分类。我们建议使用跨模态学习对H&E上以前无法标记的细胞类型进行标记。
我们利用多重免疫荧光（MxIF）组织学中固有的细胞分类信息为14个亚类创建细胞水平注释。然后，我们对MxIF进行风格迁移以合成逼真的虚拟H&E。我们使用虚拟H&E和14个亚类标签评估了监督学习方案的有效性。我们在虚拟H&E和真实H&E上对我们的模型进行了评估。
在虚拟H&E上，当使用真实质心信息时，我们能够分别以（患病率）和（患病率）的阳性预测值对辅助性T细胞和上皮祖细胞进行分类。在真实H&E上，我们需要计算有界度量而不是直接度量，因为我们的细粒度虚拟H&E预测类必须与真实H&E数据集中较粗标签中最接近的可用父类匹配。对于真实H&E，当使用真实质心信息时，我们可以分别以（父类患病率0.21）和（父类患病率0.49）的上限阳性预测值对辅助性T细胞和上皮祖细胞的有界度量进行分类。
这是第一项在H&E上为辅助性T细胞和上皮祖细胞核提供细胞类型分类的工作。
