In recent years, new and powerful microscopy and sample preparation techniques have emerged, such as light-sheet ( Huisken et al., 2004), super-resolution microscopy ( Hell and Wichmann, 1994 Gustafsson, 2000 Betzig et al., 2006 Hess et al., 2006 Rust et al., 2006), modern tissue clearing ( Dodt et al., 2007 Hama et al., 2011), or serial section scanning electron microscopy ( Denk and Horstmann, 2004 Knott et al., 2008) enabling researchers to observe biological tissues and their underlying cellular and molecular composition and dynamics in unprecedented details. Finally, LABKIT 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 LABKIT in a number of practical real-world use-cases. The ability to use pixel classifiers trained in LABKIT via the ImageJ macro language enables our users to integrate this functionality as a processing step in automated image processing workflows. Additionally, LABKIT is compatible with high performance computing (HPC) clusters for distributed processing of big image data. LABKIT is easy to install on virtually all laptops and workstations. Optionally we harness the power of graphics processing units (GPU) to gain additional runtime performance. 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. LABKIT is specifically designed to work efficiently on big image data and enables users of consumer laptops to conveniently work with multiple-terabyte images. 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. ![]() We present LABKIT, a user-friendly Fiji plugin for the segmentation of microscopy image data.
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