Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domains of Life Sciences and Deep Learning. This dataset encompasses three image collections wherein rodent neuronal cell nuclei and cytoplasm are stained with diverse markers to highlight their anatomical or functional characteristics. Specifically, we release 1874 high-resolution images alongside 750 corresponding ground-truth annotations for several learning tasks, including semantic segmentation, object detection and counting. The contribution is two-fold. First, thanks to the variety of annotations and their accessible formats, we anticipate our work will facilitate methodological advancements in computer vision approaches for segmentation, detection, feature extraction, unsupervised and self-supervised learning, transfer learning, and related areas. Second, by enabling extensive exploration and benchmarking, we hope Fluorescent Neuronal Cells v2 will catalyze breakthroughs in fluorescence microscopy analysis and promote cutting-edge discoveries in life sciences.

Clissa, L., Macaluso, A., Morelli, R., Occhinegro, A., Piscitiello, E., Taddei, L., et al. (2024). Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy. SCIENTIFIC DATA, 11(1), 184-193 [10.1038/s41597-024-03005-9].

Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy

Clissa, Luca;Macaluso, Antonio;Morelli, Roberto;Occhinegro, Alessandra;Piscitiello, Emiliana;Taddei, Ludovico;Luppi, Marco;Amici, Roberto;Cerri, Matteo;Hitrec, Timna;Rinaldi, Lorenzo;Zoccoli, Antonio
2024

Abstract

Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domains of Life Sciences and Deep Learning. This dataset encompasses three image collections wherein rodent neuronal cell nuclei and cytoplasm are stained with diverse markers to highlight their anatomical or functional characteristics. Specifically, we release 1874 high-resolution images alongside 750 corresponding ground-truth annotations for several learning tasks, including semantic segmentation, object detection and counting. The contribution is two-fold. First, thanks to the variety of annotations and their accessible formats, we anticipate our work will facilitate methodological advancements in computer vision approaches for segmentation, detection, feature extraction, unsupervised and self-supervised learning, transfer learning, and related areas. Second, by enabling extensive exploration and benchmarking, we hope Fluorescent Neuronal Cells v2 will catalyze breakthroughs in fluorescence microscopy analysis and promote cutting-edge discoveries in life sciences.
2024
Clissa, L., Macaluso, A., Morelli, R., Occhinegro, A., Piscitiello, E., Taddei, L., et al. (2024). Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy. SCIENTIFIC DATA, 11(1), 184-193 [10.1038/s41597-024-03005-9].
Clissa, Luca; Macaluso, Antonio; Morelli, Roberto; Occhinegro, Alessandra; Piscitiello, Emiliana; Taddei, Ludovico; Luppi, Marco; Amici, Roberto; Cerr...espandi
File in questo prodotto:
File Dimensione Formato  
s41597-024-03005-9.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 2.85 MB
Formato Adobe PDF
2.85 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/956794
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact