Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)

H&E Multi-Laboratory Staining Variance Exploration with Machine Learning




Julkaisun tekijätPrezja Fabi, Polonen Ilkka, Ayramo Sami, Ruusuvuori Pekka, Kuopio Teijo

KustantajaMDPI

Julkaisuvuosi2022

JournalApplied Sciences

Tietokannassa oleva lehden nimiAPPLIED SCIENCES-BASEL

Lehden akronyymiAPPL SCI-BASEL

Artikkelin numero 7511

Volyymi12

Julkaisunumero15

Sivujen määrä25

DOIhttp://dx.doi.org/10.3390/app12157511

Verkko-osoitehttps://doi.org/10.3390/app12157511

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/176244936


Tiivistelmä
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.

Ladattava julkaisu

This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.




Last updated on 2022-23-09 at 15:26