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 additional runtime performance. LABKIT is easy to install on virtually all laptops and workstations. Additionally, LABKIT is compatible with high performance computing (HPC) clusters for distributed processing of big image data. 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. 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.
Highlights
IntroductionNew 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
LABKIT is a labeling software tool designed to be intuitive and simple to use. It features a robust pixel classification algorithm aimed at segmenting images between multiple classes with very little manual labeling required
Similar to other tools of the BigDataViewer family (Pietzsch et al, 2015; Wolff et al, 2018; Hörl et al, 2019; Tischer et al, 2020), it integrates seamlessly into the SciJava and Fiji ecosystem. It can be installed through Fiji and incorporated into established workflows using ImageJ’s macro language
Summary
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. To localize objects of interest and exploit such rich datasets quantitatively, scientists need to perform image segmentation, e.g., dividing all pixels in an image into foreground pixels (part of objects of interest) and background pixels. The result of such a pixel classification is a binary mask, or a (multi-)label image if more than one foreground class is needed to discriminate different objects. Image segmentation is not an easy task to solve It is often rendered difficult by the sample’s biological variability, imperfect imaging conditions (e.g., leading to noise, blur, or other distortions), or by the complicated three-dimensional shape of the objects of interest
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