Digital pathology enables automatic analysis of histopathological sections
using artificial intelligence (AI). Automatic evaluation could improve
diagnostic efficiency and help find associations between morphological features
and clinical outcome. For development of such prediction models, identifying
invasive epithelial cells, and separating these from benign epithelial cells
and in situ lesions would be the first step. In this study, we aimed to develop
an AI model for segmentation of epithelial cells in sections from breast
cancer. We generated epithelial ground truth masks by restaining hematoxylin
and eosin (HE) sections with cytokeratin (CK) AE1/AE3, and by pathologists'
annotations. HE/CK image pairs were used to train a convolutional neural
network, and data augmentation was used to make the model more robust. Tissue
microarrays (TMAs) from 839 patients, and whole slide images from two patients
were used for training and evaluation of the models. The sections were derived
from four cohorts of breast cancer patients. TMAs from 21 patients from a fifth
cohort was used as a second test set. In quantitative evaluation, a mean Dice
score of 0.70, 0.79, and 0.75 for invasive epithelial cells, benign epithelial
cells, and in situ lesions, respectively, were achieved. In qualitative scoring
(0-5) by pathologists, results were best for all epithelium and invasive
epithelium, with scores of 4.7 and 4.4. Scores for benign epithelium and in
situ lesions were 3.7 and 2.0. The proposed model segmented epithelial cells in
HE stained breast cancer slides well, but further work is needed for accurate
division between the classes. Immunohistochemistry, together with pathologists'
annotations, enabled the creation of accurate ground truths. The model is made
freely available in FastPathology and the code is available at
this https URL

利用人工智能的数字病理学可以自动分析组织病理切片，自动评估可以提高诊断效率并帮助找出形态特征与临床结果之间的关联。本研究旨在开发一个用于乳腺癌切片上上皮细胞分割的人工智能模型，利用卷积神经网络进行训练，并通过数据增强提高模型的鲁棒性。

免疫组化指导下的乳腺癌切片中良性上皮细胞、原位病变及浸润上皮细胞的分割

Immunohistochemistry guided segmentation of benign epithelial cells, in  situ lesions, and invasive epithelial cells in breast cancer slides

Epithelial cell monolayers show remarkable displacement and velocity
correlations over distances of ten or more cell sizes that are reminiscent of
supercooled liquids and active nematics. We show that many observed features
can be described within the framework of dense active matter, and argue that
persistent uncoordinated cell motility coupled to the collective elastic modes
of the cell sheet is sufficient to produce swirl-like correlations. We obtain
this result using both continuum active linear elasticity and a normal modes
formalism, and validate analytical predictions with numerical simulations of
two agent-based cell models, soft elastic particles and the self-propelled
Voronoi model together with in-vitro experiments of confluent corneal
epithelial cell sheets. Simulations and normal mode analysis perfectly match
when tissue-level reorganisation occurs on times longer than the persistence
time of cell motility. Our analytical model quantitatively matches measured
velocity correlation functions over more than a decade with a single fitting
parameter.

研究表明，上皮细胞单层在长度超过十个细胞大小的距离上表现出类似于超冷液体和活性向列相的位移和速度相关性，这可以在密集活性物质的框架下进行描述，并且通过连续活性线性弹性学和模态分析证实，模拟和模态分析在组织层次时空重组时间大于细胞运动持续时间时是完美匹配的。