BOrg: A Brain Organoid-Based Mitosis Dataset for Automatic Analysis of Brain Diseases
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 spe...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
27.06.2024
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2406.19556 |