Annotated dataset for training deep learning models to detect astrocytes in human brain tissue.

Autor: Olar A; Eötvös Loránd University, Department of Physics of Complex Systems, Budapest, Hungary.; Eötvös Loránd University, Doctoral School of Informatics, Budapest, Hungary., Tyler T; Semmelweis University, Department of Anatomy, Histology and Embryology, Budapest, Hungary., Hoppa P; Semmelweis University, Department of Anatomy, Histology and Embryology, Budapest, Hungary., Frank E; Semmelweis University, Department of Anatomy, Histology and Embryology, Budapest, Hungary., Csabai I; Eötvös Loránd University, Department of Physics of Complex Systems, Budapest, Hungary., Adorjan I; Semmelweis University, Department of Anatomy, Histology and Embryology, Budapest, Hungary. adorjan.istvan@semmelweis.hu., Pollner P; Semmelweis University, Data-Driven Health Division of National Laboratory for Health Security, Health Services Management Training Centre, Budapest, Hungary. peter.pollner@emk.semmelweis.hu.
Jazyk: angličtina
Zdroj: Scientific data [Sci Data] 2024 Jan 19; Vol. 11 (1), pp. 96. Date of Electronic Publication: 2024 Jan 19.
DOI: 10.1038/s41597-024-02908-x
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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