Automated detection and quantification of breast cancer brain metastases in an animal model using democratized machine learning tools

Advances in digital whole-slide imaging and machine learning (ML) provide new opportunities for automated examination and quantification of histopathological slides to support pathologists and biologists. However, implementation of ML tools often requires advanced skills in computer science that may...

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Bibliographic Details
Published in:Scientific reports 2019-11, Vol.9 (1), p.17333-8, Article 17333
Main Authors: Sikpa, Dina, Fouquet, Jérémie P., Lebel, Réjean, Diamandis, Phedias, Richer, Maxime, Lepage, Martin
Format: Article
Language:English
Subjects:
631/114/1305
631/1647/794
631/67/322
64/60
692/4028/67/2321
Algorithms
Animal models
Animals
Artificial intelligence
Automation
Brain cancer
Brain Neoplasms - diagnosis
Brain Neoplasms - pathology
Brain Neoplasms - secondary
Brain tumors
Breast cancer
Breast Neoplasms - pathology
Disease Models, Animal
Female
Histopathology
Humanities and Social Sciences
Humans
Image Interpretation, Computer-Assisted - methods
Image processing
Learning algorithms
Machine learning
Metastases
Metastasis
Mice
Mice, Inbred BALB C
multidisciplinary
Neuroimaging
Science
Science (multidisciplinary)
Segmentation
Supervised Machine Learning
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Summary:Advances in digital whole-slide imaging and machine learning (ML) provide new opportunities for automated examination and quantification of histopathological slides to support pathologists and biologists. However, implementation of ML tools often requires advanced skills in computer science that may not be immediately available in the traditional wet-lab environment. Here, we propose a simple and accessible workflow to automate detection and quantification of brain epithelial metastases on digitized histological slides. We leverage 100 Hematoxylin & Eosin (H&E)-stained whole slide images (WSIs) from 25 Balb/c mice with various level of brain metastatic tumor burden. A supervised training of the Trainable Weka Segmentation (TWS) from Fiji was achieved from annotated WSIs. Upon comparison with manually drawn regions, it is apparent that the algorithm learned to identify and segment cancer cell-specific nuclei and normal brain tissue. Our approach resulted in a robust and highly concordant correlation between automated metastases quantification of brain metastases and manual human assessment (R 2  = 0.8783; P 
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-53911-x