Deep learning-based molecular morphometrics for kidney biopsies.
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ABSTRACT:
SUBMITTER: Zimmermann M
PROVIDER: S-EPMC8119189 | biostudies-literature | 2021 Apr
REPOSITORIES: biostudies-literature
Publications
Deep learning-based molecular morphometrics for kidney biopsies.
Zimmermann Marina M Klaus Martin M Wong Milagros N MN Thebille Ann-Katrin AK Gernhold Lukas L Kuppe Christoph C Halder Maurice M Kranz Jennifer J Wanner Nicola N Braun Fabian F Wulf Sonia S Wiech Thorsten T Panzer Ulf U Krebs Christian F CF Hoxha Elion E Kramann Rafael R Huber Tobias B TB Bonn Stefan S Puelles Victor G VG
JCI insight 20210408 7
Morphologic examination of tissue biopsies is essential for histopathological diagnosis. However, accurate and scalable cellular quantification in human samples remains challenging. Here, we present a deep learning-based approach for antigen-specific cellular morphometrics in human kidney biopsies, which combines indirect immunofluorescence imaging with U-Net-based architectures for image-to-image translation and dual segmentation tasks, achieving human-level accuracy. In the kidney, podocyte lo ...[more]
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