Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images
Ganz J, Lipnik K, Ammeling J, Richter B, Puget C, Parlak E, Diehl L, Klopfleisch R, Donovan TA, Kiupel M, Bertram CA, Breininger K, Aubreville M (2023)
Publication Type: Conference contribution
Publication year: 2023
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Pages Range: 226-231
Conference Proceedings Title: Informatik aktuell
Event location: Braunschweig, DEU
ISBN: 9783658416560
DOI: 10.1007/978-3-658-41657-7_49
Abstract
Nucleolar organizer regions (NORs) are parts of the DNA that are involved in RNA transcription. Due to the silver affinity of associated proteins, argyrophilic NORs (AgNORs) can be visualized using silver-based staining. The average number of AgNORs per nucleus has been shown to be a prognostic factor for predicting the outcome of many tumors. Since manual detection of AgNORs is laborious, automation is of high interest. We present a deep learning-based pipeline for automatically determining the AgNOR-score from histopathological sections. An additional annotation experiment was conducted with six pathologists to provide an independent performance evaluation of our approach. Across all raters and images, we found a mean squared error of 0.054 between the AgNOR-scores of the experts and those of the model, indicating that our approach offers performance comparable to humans.
Authors with CRIS profile
Involved external institutions
Technische Hochschule Ingolstadt
Germany (DE)
Veterinärmedizinische Universität Wien (Vetmeduni Vienna) / University of Veterinary Medicine, Vienna
Austria (AT)
Animal Medical Center (AMC)
United States (USA) (US)
Michigan State University
United States (USA) (US)
Freie Universität Berlin
Germany (DE)
Germany (DE)
Veterinärmedizinische Universität Wien (Vetmeduni Vienna) / University of Veterinary Medicine, Vienna
Austria (AT)
Animal Medical Center (AMC)
United States (USA) (US)
Michigan State University
United States (USA) (US)
Freie Universität Berlin
Germany (DE)
How to cite
APA:
Ganz, J., Lipnik, K., Ammeling, J., Richter, B., Puget, C., Parlak, E.,... Aubreville, M. (2023). Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images. In Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 226-231). Braunschweig, DEU: Springer Science and Business Media Deutschland GmbH.
MLA:
Ganz, Jonathan, et al. "Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images." Proceedings of the Bildverarbeitung für die Medizin Workshop, BVM 2023, Braunschweig, DEU Ed. Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2023. 226-231.
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