H&E Multi-Laboratory Staining Variance Exploration with Machine Learning
Prezja, F., Pölönen, I., Äyrämö, S., Ruusuvuori, P., & Kuopio, T. (2022). H&E Multi-Laboratory Staining Variance Exploration with Machine Learning. Applied Sciences, 12(15), Article 7511. https://doi.org/10.3390/app12157511
Published inApplied Sciences
DisciplineHuman and Machine based Intelligence in LearningBiologiaComputing, Information Technology and MathematicsLaskennallinen tiedeSolu- ja molekyylibiologiaTietotekniikkaHuman and Machine based Intelligence in LearningBiologyComputing, Information Technology and MathematicsComputational ScienceCell and Molecular BiologyMathematical Information Technology
© 2022 by the authors. Licensee MDPI, Basel, Switzerland
In diagnostic histopathology, hematoxylin and eosin (H&E) staining is a critical process that highlights salient histological features. Staining results vary between laboratories regardless of the histopathological task, although the method does not change. This variance can impair the accuracy of algorithms and histopathologists’ time-to-insight. Investigating this variance can help calibrate stain normalization tasks to reverse this negative potential. With machine learning, this study evaluated the staining variance between different laboratories on three tissue types. We received H&E-stained slides from 66 different laboratories. Each slide contained kidney, skin, and colon tissue samples stained by the method routinely used in each laboratory. The samples were digitized and summarized as red, green, and blue channel histograms. Dimensions were reduced using principal component analysis. The data projected by principal components were inserted into the k-means clustering algorithm and the k-nearest neighbors classifier with the laboratories as the target. The k-means silhouette index indicated that K = 2 clusters had the best separability in all tissue types. The supervised classification result showed laboratory effects and tissue-type bias. Both supervised and unsupervised approaches suggested that tissue type also affected inter-laboratory variance. We suggest tissue type to also be considered upon choosing the staining and color-normalization approach. ...
ISSN Search the Publication Forum2076-3417
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Related funder(s)Council of Tampere Region
Funding program(s)ERDF European Regional Development Fund, React-EU
Additional information about fundingThe work is related to the AI Hub Central Finland project that has received funding from Council of Tampere Region (Decision number: A75000) and European Regional Development Fund React‐EU (2014–2023) and Leverage from the EU 2014–2020. This project has been funded with support from the European Commission.
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