A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded.
Nat Biomed Eng
; 6(12): 1407-1419, 2022 12.
Article
em En
| MEDLINE
| ID: mdl-36564629
ABSTRACT
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Carcinoma Pulmonar de Células não Pequenas
/
Aprendizado Profundo
/
Neoplasias Pulmonares
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article