LABKIT: Labeling and Segmentation Toolkit for Big Image Data

Arzt, Matthias; Deschamps, J.R.; Schmied, Christopher; Pietzsch, Tobias; Schmidt, Deborah; Tomančák, Pavel; Haase, Robert; Jug, Florian

doi:10.3389/fcomp.2022.777728

articleFrontiers in Computer ScienceFeb 10, 2022GOLD OA

LABKIT: Labeling and Segmentation Toolkit for Big Image Data

MAMatthias Arzt JDJ.R. Deschamps CSChristopher Schmied TPTobias Pietzsch DSDeborah Schmidt

Center for Systems Biology Dresden · Max Planck Institute of Molecular Cell Biology and Genetics · +4 more institutions

Indexed incrossrefdoaj

Abstract

We present LABKIT, 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. LABKIT 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 well as a memory efficient and fast implementation of the random forest based pixel classification algorithm as the foundation of our software. Optionally we harness the power of graphics processing units (GPU) to gain…

Citation impact

332

total citations

FWCI: 76.29
Percentile: 100%
References: 45

Citations per year

Authors

8

Topics & keywords

Topics

Keywords

Computer science
Terabyte
Workflow
Image processing
Software
Big data
Image segmentation
Pixel

No related works found for this paper.