Fluorescent Neuronal Cells

Clissa, Luca ; Morelli, Roberto ; Squarcio, Fabio ; Hitrec, Timna ; Luppi, Marco ; Rinaldi, Lorenzo ; Cerri, Matteo ; Amici, Roberto ; Bastianini, Stefano ; Berteotti, Chiara ; Lo Martire, Viviana ; Martelli, Davide ; Occhinegro, Alessandra ; Tupone, Domenico ; Zoccoli, Giovanna ; Zoccoli, Antonio (2021) Fluorescent Neuronal Cells. University of Bologna. DOI 10.6092/unibo/amsacta/6706. [Dataset]

There is another version of this document. Click here to view it.

Full text available as:
[thumbnail of Archivio (yellow marker (Cholera Toxin sub-unit b - CTb))] Archive (Archivio (yellow marker (Cholera Toxin sub-unit b - CTb)))
License: Creative Commons: Attribution-Share Alike 4.0 (CC BY-SA 4.0)

Download (414MB)


By releasing this dataset, we aim at providing a new testbed for computer vision techniques using Deep Learning. The main peculiarity is the shift from the domain of "natural images" proper of common benchmark dataset to biological imaging. We anticipate that the advantages of doing so could be two-fold: i) fostering research in biomedical-related fields - for which popular pre-trained models perform typically poorly - and ii) promoting methodological research in deep learning by addressing peculiar requirements of these images. Possible applications include but are not limited to semantic segmentation, object detection and object counting. The data consist of 283 high-resolution pictures (1600x1200 pixels) of mice brain slices acquired through a fluorescence microscope. The final goal is to individuate and count neurons highlighted in the pictures by means of a marker, so to assess the result of a biological experiment. The corresponding ground-truth labels were generated through a hybrid approach involving semi-automatic and manual semantic segmentation. The result consists of black (0) and white (255) images having pixel-level annotations of where the stained neurons are located. For more information, please refer to Morelli, R. et al., 2021. Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet. Scientific reports, (in press). https://doi.org/10.1038/s41598-021-01929-5. The collection of original images was supported by funding from the University of Bologna (RFO 2018) and the European Space Agency (Research agreement collaboration 4000123556).

Document type
Clissa, LucaUniversity of Bologna0000-0002-4876-5200
Morelli, RobertoUniversity of Bologna0000-0001-5090-9026
Squarcio, FabioUniversity of Bologna0000-0002-6033-1042
Hitrec, TimnaUniversity of Bologna0000-0002-9296-3482
Luppi, MarcoUniversity of Bologna0000-0002-9462-5014
Rinaldi, LorenzoUniversity of Bologna0000-0001-9608-9940
Cerri, MatteoUniversity of Bologna0000-0003-3556-305X
Amici, RobertoUniversity of Bologna0000-0002-9692-2215
Bastianini, StefanoUniversity of Bologna0000-0003-2468-1704
Berteotti, ChiaraUniversity of Bologna0000-0002-4143-9445
Lo Martire, VivianaUniversity of Bologna0000-0001-8696-0835
Martelli, DavideUniversity of Bologna0000-0001-6523-9598
Occhinegro, AlessandraUniversity of Bologna
Tupone, DomenicoUniversity of Bologna0000-0002-4238-1011
Zoccoli, GiovannaUniversity of Bologna0000-0002-9670-9959
Zoccoli, AntonioUniversity of Bologna0000-0002-0993-6185
semantic segmentation; object detection; object counting; neuronal cells; fluorescent microscopy
Clissa, Luca
University of Bologna
Contact person
Deposit date
15 Nov 2021 13:43
Last modified
15 Nov 2021 13:43
Related identifier
Related identifier type
Relation type
this upload is supplement to
this upload is supplement to

Other metadata

Available versions of this document



Staff only: View the document