ArticleScientific data2024
Annotated dataset for training deep learning models to detect astrocytes in human brain tissue.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 6 citations in OpenAlex.
- High-Resolution Colony Images of Clinically Isolated Bacteria for Automated Detection and Deep Learning.Scientific data · 2026Article
- Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.Communications medicine · 2025Article
- Annotated dataset for training deep learning models to detect astrocytes in human brain tissue.Scientific data · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Astrocytes, a type of glial cell, significantly influence neuronal function, with variations in morphology and density linked to neurological disorders. Traditional methods for their accurate detection and density measurement are laborious and unsuited for large-scale operations. We introduce a dataset from human brain tissues stained with aldehyde dehydrogenase 1 family member L1 (ALDH1L1) and glial fibrillary acidic protein (GFAP). The digital whole slide images of these tissues were partitioned into 8730 patches of 500 × 500 pixels, comprising 2323 ALDH1L1 and 4714 GFAP patches at a pixel size of 0.5019/pixel, furthermore 1382 ADHD1L1 and 311 GFAP patches at 0.3557/pixel. Sourced from 16 slides and 8 patients our dataset promotes the development of tools for glial cell detection and quantification, offering insights into their density distribution in various brain areas, thereby broadening neuropathological study horizons. These samples hold value for automating detection methods, including deep learning. Derived from human samples, our dataset provides a platform for exploring astrocyte functionality, potentially guiding new diagnostic and treatment strategies for neurological disorders.
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