Nature biomedical engineering · 2022
A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Ozyoruk Kutsev Bengisu, Can Sermet, Darbaz Berkan, Başak Kayhan, Demir Derya, Gokceler Guliz Irem, Serin Gurdeniz, Hacisalihoglu Uguray Payam, Kurtuluş Emirhan, Lu Ming Y, Chen Tiffany Y, Williamson Drew F K, Yılmaz Funda, Mahmood Faisal, Turan Mehmet
Аннотация
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.