Instant processing of large-scale image data with FACT, a real-time cell segmentation and tracking algorithm.
CP: Imaging
cell tracking correction
high-throughput imaging
lineage tracking
live-cell imaging
machine-learning-based cell segmentation
real-time cell tracking
Journal
Cell reports methods
ISSN: 2667-2375
Titre abrégé: Cell Rep Methods
Pays: United States
ID NLM: 9918227360606676
Informations de publication
Date de publication:
20 Nov 2023
20 Nov 2023
Historique:
received:
18
05
2023
revised:
25
07
2023
accepted:
16
10
2023
medline:
23
11
2023
pubmed:
15
11
2023
entrez:
14
11
2023
Statut:
ppublish
Résumé
Quantifying cellular characteristics from a large heterogeneous population is essential to identify rare, disease-driving cells. A recent development in the combination of high-throughput screening microscopy with single-cell profiling provides an unprecedented opportunity to decipher disease-driving phenotypes. Accurately and instantly processing large amounts of image data, however, remains a technical challenge when an analysis output is required minutes after data acquisition. Here, we present fast and accurate real-time cell tracking (FACT). FACT can segment ∼20,000 cells in an average of 2.5 s (1.9-93.5 times faster than the state of the art). It can export quantifiable features minutes after data acquisition (independent of the number of acquired image frames) with an average of 90%-96% precision. We apply FACT to identify directionally migrating glioblastoma cells with 96% precision and irregular cell lineages from a 24 h movie with an average F1 score of 0.91.
Identifiants
pubmed: 37963463
pii: S2667-2375(23)00307-7
doi: 10.1016/j.crmeth.2023.100636
pmc: PMC10694492
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
100636Informations de copyright
Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of interests The authors declare no competing interests.
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