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Introducing Biomedisa as an open-source online platform for biomedical image segmentation

Author

Listed:
  • Philipp D. Lösel

    (Heidelberg University
    Heidelberg Institute for Theoretical Studies (HITS))

  • Thomas Kamp

    (Karlsruhe Institute of Technology (KIT)
    Karlsruhe Institute of Technology (KIT))

  • Alejandra Jayme

    (Heidelberg University
    Heidelberg Institute for Theoretical Studies (HITS))

  • Alexey Ershov

    (Karlsruhe Institute of Technology (KIT)
    Karlsruhe Institute of Technology (KIT))

  • Tomáš Faragó

    (Karlsruhe Institute of Technology (KIT))

  • Olaf Pichler

    (Heidelberg University
    Heidelberg University Computing Centre (URZ))

  • Nicholas Tan Jerome

    (Karlsruhe Institute of Technology (KIT))

  • Narendar Aadepu

    (Heidelberg University
    Karlsruhe Institute of Technology (KIT))

  • Sabine Bremer

    (Karlsruhe Institute of Technology (KIT)
    Karlsruhe Institute of Technology (KIT)
    Heidelberg University)

  • Suren A. Chilingaryan

    (Karlsruhe Institute of Technology (KIT))

  • Michael Heethoff

    (Technical University of Darmstadt)

  • Andreas Kopmann

    (Karlsruhe Institute of Technology (KIT))

  • Janes Odar

    (Karlsruhe Institute of Technology (KIT)
    Karlsruhe Institute of Technology (KIT))

  • Sebastian Schmelzle

    (Technical University of Darmstadt)

  • Marcus Zuber

    (Karlsruhe Institute of Technology (KIT)
    Karlsruhe Institute of Technology (KIT))

  • Joachim Wittbrodt

    (Heidelberg University)

  • Tilo Baumbach

    (Karlsruhe Institute of Technology (KIT)
    Karlsruhe Institute of Technology (KIT))

  • Vincent Heuveline

    (Heidelberg University
    Heidelberg Institute for Theoretical Studies (HITS)
    Heidelberg University Computing Centre (URZ))

Abstract

We present Biomedisa, a free and easy-to-use open-source online platform developed for semi-automatic segmentation of large volumetric images. The segmentation is based on a smart interpolation of sparsely pre-segmented slices taking into account the complete underlying image data. Biomedisa is particularly valuable when little a priori knowledge is available, e.g. for the dense annotation of the training data for a deep neural network. The platform is accessible through a web browser and requires no complex and tedious configuration of software and model parameters, thus addressing the needs of scientists without substantial computational expertise. We demonstrate that Biomedisa can drastically reduce both the time and human effort required to segment large images. It achieves a significant improvement over the conventional approach of densely pre-segmented slices with subsequent morphological interpolation as well as compared to segmentation tools that also consider the underlying image data. Biomedisa can be used for different 3D imaging modalities and various biomedical applications.

Suggested Citation

  • Philipp D. Lösel & Thomas Kamp & Alejandra Jayme & Alexey Ershov & Tomáš Faragó & Olaf Pichler & Nicholas Tan Jerome & Narendar Aadepu & Sabine Bremer & Suren A. Chilingaryan & Michael Heethoff & Andr, 2020. "Introducing Biomedisa as an open-source online platform for biomedical image segmentation," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19303-w
    DOI: 10.1038/s41467-020-19303-w
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