A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Nature Research
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.
Açıklama
Anahtar Kelimeler
Deep learning, Diagnosis, Generative adversarial networks, Image enhancement, Learning systems, Paraffins, Tumors, Attention mechanisms, Embedded images, High quality, Intra-operative, Learning models, Performance, Regularisation, Relevant features, Tissue images, Whole slide images, Formaldehyde, formaldehyde, paraffin, human, lung tumor, non small cell lung cancer, procedures, Carcinoma, Non-Small-Cell Lung, Deep Learning, Formaldehyde, Humans, Lung Neoplasms, Paraffin Embedding
Kaynak
Nature Biomedical Engineering
WoS Q Değeri
Scopus Q Değeri
Q1
Cilt
6
Sayı
12
