Accurate and versatile 3D segmentation of plant tissues at cellular resolution.
A. thaliana
cell segmentation
deep learning
image analysis
instance segmentation
plant biology
Journal
eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614
Informations de publication
Date de publication:
29 07 2020
29 07 2020
Historique:
received:
06
04
2020
accepted:
28
07
2020
pubmed:
30
7
2020
medline:
26
2
2021
entrez:
30
7
2020
Statut:
epublish
Résumé
Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.
Identifiants
pubmed: 32723478
doi: 10.7554/eLife.57613
pii: 57613
pmc: PMC7447435
doi:
pii:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Deutsche Forschungsgemeinschaft
ID : FOR2581
Pays : International
Organisme : Leverhulme Trust
ID : RPG-2016-049
Pays : International
Informations de copyright
© 2020, Wolny et al.
Déclaration de conflit d'intérêts
AW, LC, AV, RT, AB, ML, CW, SS, DW, RL, SS, CP, AB, SD, GB, JL, MT, FH, KS, AM, AK No competing interests declared
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