A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded
Cet article décrit le développement d'un algorithme d'apprentissage automatique qui permet d'améliorer la qualité des images des lames histologiques d'échantillons tumoraux congelés (gliomes, cancers du poumon non à petites cellules) en corrigeant les artefacts liés aux cryosections de manière à obtenir des images équivalentes à celles obtenues à partir d'échantillons tissulaires fixés au formaldéhyde et inclus en paraffine
Histological artefacts in cryosectioned tissue can hinder rapid diagnostic assessments during surgery. Formalin-fixed and paraffin-embedded (FFPE) tissue provides higher quality slides, but the process for obtaining them is laborious (typically lasting 12–48 h) and hence unsuitable for intra-operative use. Here we report the development and performance of a deep-learning model that improves the quality of cryosectioned whole-slide images by transforming them into the style of whole-slide FFPE tissue within minutes. The model consists of a generative adversarial network incorporating an attention mechanism that rectifies cryosection artefacts and a self-regularization constraint between the cryosectioned and FFPE images for the preservation of clinically relevant features. Transformed FFPE-style images of gliomas and of non-small-cell lung cancers from a dataset independent from that used to train the model improved the rates of accurate tumour subtyping by pathologists.
Nature Biomedical Engineering , résumé, 2022