Yayınlanmış 1 Ocak 2022
| Sürüm v1
Dergi makalesi
Açık
A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded
Oluşturanlar
- 1. Saglik Bilimleri Univ, Kartal Dr Lutfi Kirdar City Hosp, Dept Pathol, Istanbul, Turkey
- 2. Ege Univ, Fac Med, Dept Pathol, Izmir, Turkey
- 3. Istanbul Yeni Yuzyil Univ, Gaziosmanpasa Hosp, Pathol Dept, Med Fac, Izmir, Turkey
- 4. Stanford Univ, Stanford, CA USA
- 5. MIT, Dept Elect Engn & Comp Sci, Cambridge, MA 02138 USA
Açıklama
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.
Dosyalar
bib-e9f1270a-ada4-49d7-8287-27268cfb28dc.txt
Dosyalar
(348 Bytes)
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348 Bytes | Ön İzleme İndir |