Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy
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...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-02-01
|
| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-03005-9 |
| _version_ | 1827328671620268032 |
|---|---|
| author | Luca Clissa Antonio Macaluso Roberto Morelli Alessandra Occhinegro Emiliana Piscitiello Ludovico Taddei Marco Luppi Roberto Amici Matteo Cerri Timna Hitrec Lorenzo Rinaldi Antonio Zoccoli |
| author_facet | Luca Clissa Antonio Macaluso Roberto Morelli Alessandra Occhinegro Emiliana Piscitiello Ludovico Taddei Marco Luppi Roberto Amici Matteo Cerri Timna Hitrec Lorenzo Rinaldi Antonio Zoccoli |
| author_sort | Luca Clissa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T15:20:54Z |
| format | Article |
| id | doaj.art-59c4951e16bb4758abe11bee031e6138 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-07T15:20:54Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-59c4951e16bb4758abe11bee031e61382024-03-05T17:39:42ZengNature PortfolioScientific Data2052-44632024-02-0111111010.1038/s41597-024-03005-9Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopyLuca Clissa0Antonio Macaluso1Roberto Morelli2Alessandra Occhinegro3Emiliana Piscitiello4Ludovico Taddei5Marco Luppi6Roberto Amici7Matteo Cerri8Timna Hitrec9Lorenzo Rinaldi10Antonio Zoccoli11National Institute of Nuclear PhysicsGerman Research Center for Artificial Intelligence (DFKI), Agents and Simulated Reality DepartmentUniversity of Bologna, Department of Physics and AstronomyUniversity of Bologna, Department of Biomedical and Neuromotor SciencesUniversity of Bologna, Department of Biomedical and Neuromotor SciencesUniversity of Bologna, Department of Biomedical and Neuromotor SciencesUniversity of Bologna, Department of Biomedical and Neuromotor SciencesUniversity of Bologna, Department of Biomedical and Neuromotor SciencesUniversity of Bologna, Department of Biomedical and Neuromotor SciencesUniversity of Bologna, Department of Biomedical and Neuromotor SciencesNational Institute of Nuclear PhysicsNational Institute of Nuclear PhysicsAbstract 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.https://doi.org/10.1038/s41597-024-03005-9 |
| spellingShingle | Luca Clissa Antonio Macaluso Roberto Morelli Alessandra Occhinegro Emiliana Piscitiello Ludovico Taddei Marco Luppi Roberto Amici Matteo Cerri Timna Hitrec Lorenzo Rinaldi Antonio Zoccoli Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy Scientific Data |
| title | Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy |
| title_full | Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy |
| title_fullStr | Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy |
| title_full_unstemmed | Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy |
| title_short | Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy |
| title_sort | fluorescent neuronal cells v2 multi task multi format annotations for deep learning in microscopy |
| url | https://doi.org/10.1038/s41597-024-03005-9 |
| work_keys_str_mv | AT lucaclissa fluorescentneuronalcellsv2multitaskmultiformatannotationsfordeeplearninginmicroscopy AT antoniomacaluso fluorescentneuronalcellsv2multitaskmultiformatannotationsfordeeplearninginmicroscopy AT robertomorelli fluorescentneuronalcellsv2multitaskmultiformatannotationsfordeeplearninginmicroscopy AT alessandraocchinegro fluorescentneuronalcellsv2multitaskmultiformatannotationsfordeeplearninginmicroscopy AT emilianapiscitiello fluorescentneuronalcellsv2multitaskmultiformatannotationsfordeeplearninginmicroscopy AT ludovicotaddei fluorescentneuronalcellsv2multitaskmultiformatannotationsfordeeplearninginmicroscopy AT marcoluppi fluorescentneuronalcellsv2multitaskmultiformatannotationsfordeeplearninginmicroscopy AT robertoamici fluorescentneuronalcellsv2multitaskmultiformatannotationsfordeeplearninginmicroscopy AT matteocerri fluorescentneuronalcellsv2multitaskmultiformatannotationsfordeeplearninginmicroscopy AT timnahitrec fluorescentneuronalcellsv2multitaskmultiformatannotationsfordeeplearninginmicroscopy AT lorenzorinaldi fluorescentneuronalcellsv2multitaskmultiformatannotationsfordeeplearninginmicroscopy AT antoniozoccoli fluorescentneuronalcellsv2multitaskmultiformatannotationsfordeeplearninginmicroscopy |