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bioRxiv · 10.1101/2021.10.14.464362

Labkit: Labeling and Segmentation Toolkit for Big Image Data

Arzt, M.·Deschamps, J.·Schmied, C.·Pietzsch, T.·Schmidt, D.·Haase, R.·Jug, F.
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Abstract

We present LO_SCPLOWABKITC_SCPLOW, 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. LO_SCPLOWABKITC_SCPLOW 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. LO_SCPLOWABKITC_SCPLOW is easy to install on virtually all laptops and workstations. Additionally, LO_SCPLOWABKITC_SCPLOW is compatible with high performance computing (HPC) clusters for distributed processing of big image data. The ability to use pixel classifiers trained in LO_SCPLOWABKITC_SCPLOW 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, LO_SCPLOWABKITC_SCPLOW 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 LO_SCPLOWABKITC_SCPLOW in a number of practical real-world use-cases.

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Arzt, M., Deschamps, J., Schmied, C., Pietzsch, T., Schmidt, D., Haase, R., Jug, F.. 2021-10-15. Labkit: Labeling and Segmentation Toolkit for Big Image Data. https://doi.org/10.1101/2021.10.14.464362

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