Deep Learning-Based Automatic Assessment of AgNOR-scores in Histopathology Images

Jonathan Ganz; Karoline Lipnik; Jonas Ammeling; Barbara Richter; Chloé Puget; Eda Parlak; Laura Diehl; Robert Klopfleisch; Taryn A. Donovan; Matti Kiupel; Christof A. Bertram; Katharina Breininger; Marc Aubreville

arXiv · 2212.07721

Deep Learning-Based Automatic Assessment of AgNOR-scores in Histopathology Images

Jonathan Ganz·Karoline Lipnik·Jonas Ammeling·Barbara Richter·Chloé Puget·Eda Parlak·Laura Diehl·Robert Klopfleisch·Taryn A. Donovan·Matti Kiupel·Christof A. Bertram·Katharina Breininger·Marc Aubreville

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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.

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Jonathan Ganz, Karoline Lipnik, Jonas Ammeling, Barbara Richter, Chloé Puget, Eda Parlak, Laura Diehl, Robert Klopfleisch, Taryn A. Donovan, Matti Kiupel, Christof A. Bertram, Katharina Breininger, Marc Aubreville. 2022-12-15. Deep Learning-Based Automatic Assessment of AgNOR-scores in Histopathology Images. https://arxiv.org/abs/2212.07721

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