ArticleScientific reports2024
Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A semi-automated pipeline integrating ImageJ/Fiji and StarDist for the reproducible quantification of cellular and optical density metrics in immunofluorescence images of brain tissue.Frontiers in neuroanatomy · 2026Article
- Stereology with OPEN-Stereo: low-cost, accessible, and accurate cellular quantification.Scientific reports · 2025Article
- Myelinated fiber labeling and orientation mapping of the human brain with light-sheet fluorescence microscopy.NeuroImage · 2025Article
- Quantitative cytoarchitectural phenotyping of deparaffinized human brain tissues.Communications biology · 2025Article
- Mapping cellular stress and lipid dysregulation in Alzheimer-related progressive neurodegeneration using label-free Raman microscopy.Communications biology · 2024Article
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10 authors.
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Abstract
3D reconstruction of human brain volumes at high resolution is now possible thanks to advancements in tissue clearing methods and fluorescence microscopy techniques. Analyzing the massive data produced with these approaches requires automatic methods able to perform fast and accurate cell counting and localization. Recent advances in deep learning have enabled the development of various tools for cell segmentation. However, accurate quantification of neurons in the human brain presents specific challenges, such as high pixel intensity variability, autofluorescence, non-specific fluorescence and very large size of data. In this paper, we provide a thorough empirical evaluation of three techniques based on deep learning (StarDist, CellPose and BCFind-v2, an updated version of BCFind) using a recently introduced three-dimensional stereological design as a reference for large-scale insights. As a representative problem in human brain analysis, we focus on a
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