High-throughput image analysis in the biomedical domain has gained significant attention in recent years, driving advancements in drug discovery, disease prediction, and personalized medicine. Organoids, specifically, are an active area of research, providing excellent models for human organs and their functions. Automating the quantification of organoids in microscopy images would provide an effective solution to overcome substantial manual quantification bottlenecks, particularly in high-throughput image analysis. However, there is a notable lack of open biomedical datasets, in contrast to other domains, such as autonomous driving, and, notably, only few of them have attempted to quantify annotation uncertainty. In this work, we present MultiOrg a comprehensive organoid dataset tailored for object detection tasks with uncertainty quantification. This dataset comprises over 400 high-resolution 2d microscopy images and curated annotations of more than 60,000 organoids. Most importantly, it includes three label sets for the test data, independently annotated by two experts at distinct time points. We additionally provide a benchmark for organoid detection, and make the best model available through an easily installable, interactive plugin for the popular image visualization tool Napari, to perform organoid quantification.
@article{arxiv.2410.14612,
title = {MultiOrg: A Multi-rater Organoid-detection Dataset},
author = {Christina Bukas and Harshavardhan Subramanian and Fenja See and Carina Steinchen and Ivan Ezhov and Gowtham Boosarpu and Sara Asgharpour and Gerald Burgstaller and Mareike Lehmann and Florian Kofler and Marie Piraud},
journal= {arXiv preprint arXiv:2410.14612},
year = {2024}
}