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

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, Andreas Kopmann, Janes Odar, Sebastian Schmelzle, Marcus Zuber, Joachim Wittbrodt, Tilo Baumbach and Vincent Heuveline
Additional contact information
Philipp D. Lösel: Heidelberg University
Thomas Kamp: Karlsruhe Institute of Technology (KIT)
Alejandra Jayme: Heidelberg University
Alexey Ershov: Karlsruhe Institute of Technology (KIT)
Tomáš Faragó: Karlsruhe Institute of Technology (KIT)
Olaf Pichler: Heidelberg University
Nicholas Tan Jerome: Karlsruhe Institute of Technology (KIT)
Narendar Aadepu: Heidelberg University
Sabine Bremer: Karlsruhe Institute of Technology (KIT)
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)
Sebastian Schmelzle: Technical University of Darmstadt
Marcus Zuber: Karlsruhe Institute of Technology (KIT)
Joachim Wittbrodt: Heidelberg University
Tilo Baumbach: Karlsruhe Institute of Technology (KIT)
Vincent Heuveline: Heidelberg University

Nature Communications, 2020, vol. 11, issue 1, 1-14

Abstract: 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.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-020-19303-w Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19303-w

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-19303-w

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().