A large and diverse brain organoid dataset of 1,400 cross-laboratory images of 64 trackable brain organoids

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-...

Full description

Saved in:
Bibliographic Details
Published in:Scientific data 2024-05, Vol.11 (1), p.514-514, Article 514
Main Authors: Schröter, Julian, Deininger, Luca, Lupse, Blaz, Richter, Petra, Syrbe, Steffen, Mikut, Ralf, Jung-Klawitter, Sabine
Format: Article
Language:English
Subjects:
631/114/1305
631/136/1425
Brain - cytology
Brain - diagnostic imaging
Data Descriptor
Datasets
Humanities and Social Sciences
Humans
Microencephaly
multidisciplinary
Neurodevelopmental disorders
Neuroimaging
Organoids
Organoids - cytology
Science
Science (multidisciplinary)
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-024-03330-z