BOrg: A Brain Organoid-Based Mitosis Dataset for Automatic Analysis of Brain Diseases
Muhammad Awais, Mehaboobathunnisa Sahul Hameed, Bidisha Bhattacharya,, Orly Reiner, Rao Muhammad Anwer

TL;DR
This paper introduces BOrg, a specialized dataset with an annotation pipeline for analyzing mitosis in brain organoids, enabling improved automated detection and analysis of cell division stages relevant to neurodevelopmental research.
Contribution
The paper presents BOrg, a novel brain organoid mitosis dataset with an efficient annotation method and benchmarks adapted models for mitosis detection, advancing automated analysis in neurodevelopmental studies.
Findings
State-of-the-art models significantly improve mitosis detection accuracy.
Automated tools developed from BOrg facilitate neurodevelopmental research.
Benchmark results demonstrate enhanced efficiency over existing methods.
Abstract
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…
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Taxonomy
TopicsMachine Learning in Bioinformatics · Brain Tumor Detection and Classification · Gene expression and cancer classification
