FOCUST: Fluorescent Object and Cell gpU-Accelerated Segmentation Toolbox

Area of Science:

• * Biological imaging and analysis
• * Computational biology
• * Microscopy data processing

Background:

• * Segmentation of biological compartments (e.g., cells, nuclei) is crucial for analyzing 3D fluorescent datasets.
• * Existing open-source tools often lack hierarchical labeling, user-friendliness, and 3D-specific accelerated processing.
• * Difficulty in analysis hinders efficient quantification and reproducibility in volumetric biological data.

Purpose of the Study:

• * To develop an accessible, open-source software tool for hierarchical segmentation and analysis of 3D fluorescent microscopy data.
• * To provide GPU-accelerated image processing and intuitive user interfaces for researchers.
• * To enhance the reproducibility and robustness of 3D cellular dataset analysis.

Main Methods:

• * Development of the Fluorescent Object and Cell gpU-accelerated Segmentation Toolbox (FOCUST) as a plugin for FIJI/ImageJ.
• * Implementation of intuitive user interfaces for optimization, segmentation, and quantification of 3D cellular datasets.
• * Integration of features for parameter saving/loading, batch processing, hierarchical labeling, stratification, and skeletonization.

Main Results:

• * FOCUST offers streamlined 3D analysis with optimized pre-processing and segmentation parameter configuration.
• * The software supports hierarchical labeling for common sample types and batch processing for efficient analysis.
• * Key features include reproducibility enhancements, 3D label manipulation, and compatibility with external segmentation outputs.

Conclusions:

• * FOCUST significantly improves the accessibility, reproducibility, and robustness of 3D fluorescent dataset analysis.
• * The toolbox addresses limitations in existing software by providing specialized 3D analyses and GPU acceleration.
• * FOCUST empowers researchers to perform complex cellular and subcellular quantification more efficiently.