Accurate classification of major brain cell types using in vivo imaging and neural network processing

• Verfasst von: Knabbe, Johannes [VerfasserIn]
• Titel: Accurate classification of major brain cell types using in vivo imaging and neural network processing
• Verf.angabe: Johannes Knabbe
• Verlagsort: Heidelberg
• Verlag: Universität
• E-Jahr: 2023
• Jahr: 2023-09-18
• Umfang: 1 Online-Ressource (6 Files)
• Fussnoten: Gesehen am 20.09.2023
• DOI: doi:10.11588/data/L3PITA
• Datenträger: Online-Ressource
• Dokumenttyp: Forschungsdaten
• Dokumenttyp: Datenbank
• Sprache: eng
• Bibliogr. Hinweis: Forschungsdaten zu: Das Gupta, Amrita, 1992 - : Comprehensive monitoring of tissue composition using in vivo imaging of cell nuclei and deep learning
• Sach-SW: Health and Life Sciences
• Sach-SW: Medicine
• K10plus-PPN: 1860046843
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

This dataset accompanies the article of the same title in the journal Plos Biology. It includes a) Ground truth datasets for the training of the StarDist neuronal network for nucleus segmentation (StardistTraining.tar.gz) b) The trained Stardist nucleus segmentation model (StardistModel.tar.gz c) raw and segmented data for the training of the cell type classification (CelltypeClassification.tar.gz, CelltypeClassificationExcInhNeurons.tar.gz) d) the raw and segmented data for the results of the paper (RawdataResults.tar.gz) e) Ground truth data for the training of all classifiers (ClassificationTrainingDataSet.tab)