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Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Item Type:Review
Title:Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets
Creators Name:Müller, A. and Schmidt, D. and Albrecht, J.P. and Rieckert, L. and Otto, M. and Galicia Garcia, L.E. and Fabig, G. and Solimena, M. and Weigert, M.
Abstract:Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and spatial analysis is necessary. Here we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. The procedures are aimed at researchers in the life sciences with modest computational expertise, who use volume electron microscopy and need to generate three-dimensional (3D) segmentation labels for different types of cell organelles while minimizing manual annotation efforts, to analyze the spatial interactions between organelle instances and to visualize the 3D segmentation results. We provide detailed guidelines for choosing well-suited segmentation tools for specific cell organelles, and to bridge compatibility issues between freely available open-source tools, we distribute the critical steps as easily installable Album solutions for deep learning segmentation, spatial analysis and 3D rendering. Our detailed description can serve as a reference for similar projects requiring particular strategies for single- or multiple-organelle analysis, which can be achieved with computational resources commonly available to single-user setups.
Keywords:Image Processing, Machine Learning, Scanning Electron Microscopy
Source:Nature Protocols
Publisher:Nature Publishing Group
Date:29 February 2024
Official Publication:https://doi.org/10.1038/s41596-024-00957-5
PubMed:View item in PubMed

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