A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
H&E Multi-Laboratory Staining Variance Exploration with Machine Learning (2022)


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


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajatPrezja, Fabi; Pölönen, Ilkka; Äyrämö, Sami; Ruusuvuori, Pekka; Kuopio, Teijo

Lehti tai sarjaApplied Sciences

eISSN2076-3417

Julkaisuvuosi2022

Ilmestymispäivä26.07.2022

Volyymi12

Lehden numero15

Artikkelinumero7511

KustantajaMDPI

JulkaisumaaSveitsi

Julkaisun kielienglanti

DOIhttps://doi.org/10.3390/app12157511

Julkaisun avoin saatavuusAvoimesti saatavilla

Julkaisukanavan avoin saatavuusKokonaan avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX)https://jyx.jyu.fi/handle/123456789/82586


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.


YSO-asiasanatlaboratoriotekniikkadiagnostiikkakoneoppiminentekoälypatologiahistologianäytteetkudoksetväriaineetkuvantaminen

Vapaat asiasanatHE-värjäys; hematoksyliini-eosiini-värjäys; histopatologia


Liittyvät organisaatiot


Hankkeet, joissa julkaisu on tehty


OKM-raportointiKyllä

Raportointivuosi2022

JUFO-taso1


Viimeisin päivitys 2024-15-06 klo 22:25