Labkit: Labeling and Segmentation Toolkit for Big Image Data Article Swipe
Matthias Arzt
,
J.R. Deschamps
,
Christopher Schmied
,
Tobias Pietzsch
,
Deborah Schmidt
,
Robert Haase
,
Florian Jug
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.10.14.464362
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.10.14.464362
We present L abkit , a user-friendly Fiji plugin for the segmentation of microscopy image data. It offers easy to use manual and automated image segmentation routines that can be rapidly applied to single- and multi-channel images as well as to timelapse movies in 2D or 3D. L abkit is specifically designed to work efficiently on big image data and enables users of consumer laptops to conveniently work with multiple-terabyte images. This efficiency is achieved by using ImgLib2 and BigDataViewer as the foundation of our software. Furthermore, memory efficient and fast random forest based pixel classification inspired by the Waikato Environment for Knowledge Analysis (Weka) is implemented. Optionally we harness the power of graphics processing units (GPU) to gain additional runtime performance. L abkit is easy to install on virtually all laptops and workstations. Additionally, L abkit is compatible with high performance computing (HPC) clusters for distributed processing of big image data. The ability to use pixel classifiers trained in L abkit via the ImageJ macro language enables our users to integrate this functionality as a processing step in automated image processing workflows. Last but not least, L abkit comes with rich online resources such as tutorials and examples that will help users to familiarize themselves with available features and how to best use L abkit in a number of practical real-world use-cases.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.10.14.464362
- https://www.biorxiv.org/content/biorxiv/early/2021/10/15/2021.10.14.464362.full.pdf
- OA Status
- green
- Cited By
- 23
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3207334748
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3207334748Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2021.10.14.464362Digital Object Identifier
- Title
-
Labkit: Labeling and Segmentation Toolkit for Big Image DataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-15Full publication date if available
- Authors
-
Matthias Arzt, J.R. Deschamps, Christopher Schmied, Tobias Pietzsch, Deborah Schmidt, Robert Haase, Florian JugList of authors in order
- Landing page
-
https://doi.org/10.1101/2021.10.14.464362Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2021/10/15/2021.10.14.464362.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/10/15/2021.10.14.464362.full.pdfDirect OA link when available
- Concepts
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Computer science, Terabyte, Workflow, Image processing, Big data, Software, Segmentation, Pixel, Image segmentation, Plug-in, Workstation, Graphics, Artificial intelligence, Computer graphics (images), Image (mathematics), Database, Data mining, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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23Total citation count in OpenAlex
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2025: 2, 2024: 9, 2023: 10, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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51Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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