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Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets.
Müller, Andreas; Schmidt, Deborah; Albrecht, Jan Philipp; Rieckert, Lucas; Otto, Maximilian; Galicia Garcia, Leticia Elizabeth; Fabig, Gunar; Solimena, Michele; Weigert, Martin.
Afiliación
  • Müller A; Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany. andreas.mueller1@tu-dresden.de.
  • Schmidt D; Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany. andreas.mueller1@tu-dresden.de.
  • Albrecht JP; German Center for Diabetes Research, Neuherberg, Germany. andreas.mueller1@tu-dresden.de.
  • Rieckert L; HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany. deborah.schmidt@mdc-berlin.de.
  • Otto M; HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany.
  • Galicia Garcia LE; Humboldt-Universität zu Berlin, Faculty of Mathematics and Natural Sciences, Berlin, Germany.
  • Fabig G; HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany.
  • Solimena M; HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany.
  • Weigert M; Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany.
Nat Protoc ; 19(5): 1436-1466, 2024 May.
Article en En | MEDLINE | ID: mdl-38424188
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Microscopía Electrónica / Imagenología Tridimensional Límite: Humans Idioma: En Revista: Nat Protoc Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Microscopía Electrónica / Imagenología Tridimensional Límite: Humans Idioma: En Revista: Nat Protoc Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido