Griottes: a generalist tool for network generation from segmented tissue images.
Graphs
Image analysis
Napari
Python
Single-cell imaging
Spatial analysis
Tissue imaging
Journal
BMC biology
ISSN: 1741-7007
Titre abrégé: BMC Biol
Pays: England
ID NLM: 101190720
Informations de publication
Date de publication:
11 08 2022
11 08 2022
Historique:
received:
18
01
2022
accepted:
15
07
2022
entrez:
11
8
2022
pubmed:
12
8
2022
medline:
16
8
2022
Statut:
epublish
Résumé
Microscopy techniques and image segmentation algorithms have improved dramatically this decade, leading to an ever increasing amount of biological images and a greater reliance on imaging to investigate biological questions. This has created a need for methods to extract the relevant information on the behaviors of cells and their interactions, while reducing the amount of computing power required to organize this information. This task can be performed by using a network representation in which the cells and their properties are encoded in the nodes, while the neighborhood interactions are encoded by the links. Here, we introduce Griottes, an open-source tool to build the "network twin" of 2D and 3D tissues from segmented microscopy images. We show how the library can provide a wide range of biologically relevant metrics on individual cells and their neighborhoods, with the objective of providing multi-scale biological insights. The library's capacities are demonstrated on different image and data types. This library is provided as an open-source tool that can be integrated into common image analysis workflows to increase their capacities.
Sections du résumé
BACKGROUND
Microscopy techniques and image segmentation algorithms have improved dramatically this decade, leading to an ever increasing amount of biological images and a greater reliance on imaging to investigate biological questions. This has created a need for methods to extract the relevant information on the behaviors of cells and their interactions, while reducing the amount of computing power required to organize this information.
RESULTS
This task can be performed by using a network representation in which the cells and their properties are encoded in the nodes, while the neighborhood interactions are encoded by the links. Here, we introduce Griottes, an open-source tool to build the "network twin" of 2D and 3D tissues from segmented microscopy images. We show how the library can provide a wide range of biologically relevant metrics on individual cells and their neighborhoods, with the objective of providing multi-scale biological insights. The library's capacities are demonstrated on different image and data types.
CONCLUSIONS
This library is provided as an open-source tool that can be integrated into common image analysis workflows to increase their capacities.
Identifiants
pubmed: 35953853
doi: 10.1186/s12915-022-01376-2
pii: 10.1186/s12915-022-01376-2
pmc: PMC9367069
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
178Informations de copyright
© 2022. The Author(s).
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