Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features.
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Embargo End Date
ICR Authors
Authors
Juppet, Q
De Martino, F
Marcandalli, E
Weigert, M
Burri, O
Unser, M
Brisken, C
Sage, D
De Martino, F
Marcandalli, E
Weigert, M
Burri, O
Unser, M
Brisken, C
Sage, D
Document Type
Journal Article
Date
2021-06-01
Date Accepted
2021-04-05
Abstract
Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cell contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E- and DAPI-stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier .
Citation
Journal of mammary gland biology and neoplasia, 2021, 26 (2), pp. 101 - 112
Source Title
Publisher
SPRINGER/PLENUM PUBLISHERS
ISSN
1083-3021
eISSN
1573-7039
Collections
Research Team
Endocrine control mechanisms
Endocrine control mechanisms
Endocrine control mechanisms