Journal of Pathology Informatics Journal of Pathology Informatics
Contact us | Home | Login   |  Users Online: 586  Print this pageEmail this pageSmall font sizeDefault font sizeIncrease font size 

Year : 2022  |  Volume : 13  |  Issue : 1  |  Page : 6

On the acceptance of “fake” histopathology: A study on frozen sections optimized with deep learning

1 Department of Information Technology and System Management, Salzburg University of Applied Sciences, Salzburg, Austria
2 Department of Biomedical Sciences, Salzburg University of Applied Sciences, Salzburg, Austria
3 Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, Austria
4 Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany; Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Germany
5 Institute of Pathology, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
6 Patholab Salzburg, Salzburg, Austria
7 Department of Pathology and Microbiology, Kardinal Schwarzenberg Klinikum, Schwarzach, Austria

Correspondence Address:
Dr. Michael Gadermayr
Department of Information Technology and System Management, Salzburg University of Applied Sciences, Urstein Sud 1, 5412 Puch.
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jpi.jpi_53_21

Rights and Permissions

Background: The fast acquisition process of frozen sections allows surgeons to wait for histological findings during the interventions to base intrasurgical decisions on the outcome of the histology. Compared with paraffin sections, however, the quality of frozen sections is often strongly reduced, leading to a lower diagnostic accuracy. Deep neural networks are capable of modifying specific characteristics of digital histological images. Particularly, generative adversarial networks proved to be effective tools to learn about translation between two modalities, based on two unconnected data sets only. The positive effects of such deep learning-based image optimization on computer-aided diagnosis have already been shown. However, since fully automated diagnosis is controversial, the application of enhanced images for visual clinical assessment is currently probably of even higher relevance. Methods: Three different deep learning-based generative adversarial networks were investigated. The methods were used to translate frozen sections into virtual paraffin sections. Overall, 40 frozen sections were processed. For training, 40 further paraffin sections were available. We investigated how pathologists assess the quality of the different image translation approaches and whether experts are able to distinguish between virtual and real digital pathology. Results: Pathologists’ detection accuracy of virtual paraffin sections (from pairs consisting of a frozen and a paraffin section) was between 0.62 and 0.97. Overall, in 59% of images, the virtual section was assessed as more appropriate for a diagnosis. In 53% of images, the deep learning approach was preferred to conventional stain normalization (SN). Conclusion: Overall, expert assessment indicated slightly improved visual properties of converted images and a high similarity to real paraffin sections. The observed high variability showed clear differences in personal preferences.

Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)

 Article Access Statistics
    PDF Downloaded171    
    Comments [Add]    

Recommend this journal