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Deep learning-enabled realistic virtual histology with ultraviolet photoacoustic remote sensing microscopy.
Martell, Matthew T; Haven, Nathaniel J M; Cikaluk, Brendyn D; Restall, Brendon S; McAlister, Ewan A; Mittal, Rohan; Adam, Benjamin A; Giannakopoulos, Nadia; Peiris, Lashan; Silverman, Sveta; Deschenes, Jean; Li, Xingyu; Zemp, Roger J.
Afiliación
  • Martell MT; Department of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada.
  • Haven NJM; Department of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada.
  • Cikaluk BD; Department of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada.
  • Restall BS; Department of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada.
  • McAlister EA; Department of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada.
  • Mittal R; Department of Laboratory Medicine and Pathology, University of Alberta, 11405 87 Avenue NW, Edmonton, AB, T6G 1C9, Canada.
  • Adam BA; Department of Laboratory Medicine and Pathology, University of Alberta, 11405 87 Avenue NW, Edmonton, AB, T6G 1C9, Canada.
  • Giannakopoulos N; Department of Laboratory Medicine and Pathology, University of Alberta, 11405 87 Avenue NW, Edmonton, AB, T6G 1C9, Canada.
  • Peiris L; Department of Surgery, University of Alberta, 8440 - 112 Street, Edmonton, AB, T6G 2B7, Canada.
  • Silverman S; Department of Laboratory Medicine and Pathology, University of Alberta, 11405 87 Avenue NW, Edmonton, AB, T6G 1C9, Canada.
  • Deschenes J; Department of Laboratory Medicine and Pathology, University of Alberta, 11405 87 Avenue NW, Edmonton, AB, T6G 1C9, Canada.
  • Li X; Department of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada.
  • Zemp RJ; Department of Electrical and Computer Engineering, University of Alberta, 116 Street & 85 Avenue, Edmonton, AB, T6G 2R3, Canada. rzemp@ualberta.ca.
Nat Commun ; 14(1): 5967, 2023 09 25.
Article en En | MEDLINE | ID: mdl-37749108
The goal of oncologic surgeries is complete tumor resection, yet positive margins are frequently found postoperatively using gold standard H&E-stained histology methods. Frozen section analysis is sometimes performed for rapid intraoperative margin evaluation, albeit with known inaccuracies. Here, we introduce a label-free histological imaging method based on an ultraviolet photoacoustic remote sensing and scattering microscope, combined with unsupervised deep learning using a cycle-consistent generative adversarial network for realistic virtual staining. Unstained tissues are scanned at rates of up to 7 mins/cm2, at resolution equivalent to 400x digital histopathology. Quantitative validation suggests strong concordance with conventional histology in benign and malignant prostate and breast tissues. In diagnostic utility studies we demonstrate a mean sensitivity and specificity of 0.96 and 0.91 in breast specimens, and respectively 0.87 and 0.94 in prostate specimens. We also find virtual stain quality is preferred (P = 0.03) compared to frozen section analysis in a blinded survey of pathologists.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Microscopía Límite: Humans / Male Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2023 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Microscopía Límite: Humans / Male Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2023 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Reino Unido