BOrg: A Brain Organoid-Based Mitosis Dataset for Automatic Analysis of Brain Diseases
This work addresses the time-consuming manual analysis and lack of specific datasets for brain organoid research, aiding in the study of neurodevelopmental disorders, but it is incremental as it adapts existing methods to a new dataset.
The authors tackled the problem of manually analyzing mitosis in brain organoids for neurodevelopmental disorder studies by introducing the BOrg dataset, which uses an efficient annotation pipeline and adapted models to significantly improve efficiency and accuracy in mitosis analysis.
Recent advances have enabled the study of human brain development using brain organoids derived from stem cells. Quantifying cellular processes like mitosis in these organoids offers insights into neurodevelopmental disorders, but the manual analysis is time-consuming, and existing datasets lack specific details for brain organoid studies. We introduce BOrg, a dataset designed to study mitotic events in the embryonic development of the brain using confocal microscopy images of brain organoids. BOrg utilizes an efficient annotation pipeline with sparse point annotations and techniques that minimize expert effort, overcoming limitations of standard deep learning approaches on sparse data. We adapt and benchmark state-of-the-art object detection and cell counting models on BOrg for detecting and analyzing mitotic cells across prophase, metaphase, anaphase, and telophase stages. Our results demonstrate these adapted models significantly improve mitosis analysis efficiency and accuracy for brain organoid research compared to existing methods. BOrg facilitates the development of automated tools to quantify statistics like mitosis rates, aiding mechanistic studies of neurodevelopmental processes and disorders. Data and code are available at https://github.com/awaisrauf/borg.