A large and diverse brain organoid dataset of 1,400 cross-laboratory images of 64 trackable brain organoids.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
20 May 2024
20 May 2024
Historique:
received:
05
05
2023
accepted:
01
05
2024
medline:
21
5
2024
pubmed:
21
5
2024
entrez:
20
5
2024
Statut:
epublish
Résumé
Brain organoids represent a useful tool for modeling of neurodevelopmental disorders and can recapitulate brain volume alterations such as microcephaly. To monitor organoid growth, brightfield microscopy images are frequently used and evaluated manually which is time-consuming and prone to observer-bias. Recent software applications for organoid evaluation address this issue using classical or AI-based methods. These pipelines have distinct strengths and weaknesses that are not evident to external observers. We provide a dataset of more than 1,400 images of 64 trackable brain organoids from four clones differentiated from healthy and diseased patients. This dataset is especially powerful to test and compare organoid analysis pipelines because of (1) trackable organoids (2) frequent imaging during development (3) clone diversity (4) distinct clone development (5) cross sample imaging by two different labs (6) common imaging distractors, and (6) pixel-level ground truth organoid annotations. Therefore, this dataset allows to perform differentiated analyses to delineate strengths, weaknesses, and generalizability of automated organoid analysis pipelines as well as analysis of clone diversity and similarity.
Identifiants
pubmed: 38769371
doi: 10.1038/s41597-024-03330-z
pii: 10.1038/s41597-024-03330-z
doi:
Types de publication
Dataset
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
514Subventions
Organisme : Dietmar Hopp Stiftung
ID : 1DH1813319
Informations de copyright
© 2024. The Author(s).
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