A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Unstained Tissue Imaging and Virtual Hematoxylin and Eosin Staining of Histologic Whole Slide Images
Tekijät: Koivukoski Sonja, Khan Umair, Ruusuvuori Pekka, Latonen Leena
Kustantaja: ELSEVIER SCIENCE INC
Julkaisuvuosi: 2023
Journal: Laboratory Investigation
Tietokannassa oleva lehden nimi: LABORATORY INVESTIGATION
Lehden akronyymi: LAB INVEST
Artikkelin numero: 100070
Vuosikerta: 103
Numero: 5
Sivujen määrä: 12
ISSN: 0023-6837
DOI: https://doi.org/10.1016/j.labinv.2023.100070
Verkko-osoite: https://doi.org/10.1016/j.labinv.2023.100070
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/179464866
Tissue structures, phenotypes, and pathology are routinely investigated based on histology. This includes chemically staining the transparent tissue sections to make them visible to the human eye. Although chemical staining is fast and routine, it permanently alters the tissue and often consumes hazardous reagents. On the other hand, on using adjacent tissue sections for combined measurements, the cell-wise resolution is lost owing to sections representing different parts of the tissue. Hence, techniques providing visual information of the basic tissue structure enabling additional measurements from the exact same tissue section are required. Here we tested un-stained tissue imaging for the development of computational hematoxylin and eosin (HE) staining. We used unsupervised deep learning (CycleGAN) and whole slide images of prostate tissue sec-tions to compare the performance of imaging tissue in paraffin, as deparaffinized in air, and as deparaffinized in mounting medium with section thicknesses varying between 3 and 20 mm. We showed that although thicker sections increase the information content of tissue structures in the images, thinner sections generally perform better in providing information that can be reproduced in virtual staining. According to our results, tissue imaged in paraffin and as deparaffinized pro-vides a good overall representation of the tissue for virtually HE-stained images. Further, using a pix2pix model, we showed that the reproduction of overall tissue histology can be clearly improved with image-to-image translation using supervised learning and pixel-wise ground truth. We also showed that virtual HE staining can be used for various tissues and used with both 20x and 40x imaging magnifications. Although the performance and methods of virtual staining need further development, our study provides evidence of the feasibility of whole slide unstained microscopy as a fast, cheap, and feasible approach to producing virtual staining of tissue histology while sparing the exact same tissue section ready for subsequent utilization with follow-up methods at single-cell resolution.(c) 2023 THE AUTHORS. Published by Elsevier Inc. on behalf of the United States & Canadian Academy of Pathology. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Ladattava julkaisu This is an electronic reprint of the original article. |  |
