Evidence map›Paper›PMID 38341463›Full record

ArticleScientific data2024

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

Luca Clissa, Antonio Macaluso, Roberto Morelli, Alessandra Occhinegro, Emiliana Piscitiello, Ludovico Taddei, Marco Luppi, Roberto Amici, Matteo Cerri, Timna Hitrec and 2 more

Open access · goldAbstract readDataset
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
4.0field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 2 countries.

Luca ClissaNational Institute of Nuclear Physics, Bologna, Italy. luca.clissa2@unibo.it.ORCID 0000-0002-4876-5200
Antonio MacalusoGerman Research Center for Artificial Intelligence (DFKI), Agents and Simulated Reality Department, Saarbruecken, Germany.ORCID 0000-0002-1348-250X
Roberto MorelliUniversity of Bologna, Department of Physics and Astronomy, Bologna, Italy.
Alessandra OcchinegroUniversity of Bologna, Department of Biomedical and Neuromotor Sciences, Bologna, Italy.
Emiliana PiscitielloUniversity of Bologna, Department of Biomedical and Neuromotor Sciences, Bologna, Italy.
Ludovico TaddeiUniversity of Bologna, Department of Biomedical and Neuromotor Sciences, Bologna, Italy.
Marco LuppiUniversity of Bologna, Department of Biomedical and Neuromotor Sciences, Bologna, Italy.ORCID 0000-0002-9462-5014
Roberto AmiciUniversity of Bologna, Department of Biomedical and Neuromotor Sciences, Bologna, Italy.
Matteo CerriUniversity of Bologna, Department of Biomedical and Neuromotor Sciences, Bologna, Italy.ORCID 0000-0003-3556-305X
Timna HitrecUniversity of Bologna, Department of Biomedical and Neuromotor Sciences, Bologna, Italy.
Lorenzo RinaldiNational Institute of Nuclear Physics, Bologna, Italy.
Antonio ZoccoliNational Institute of Nuclear Physics, Bologna, Italy.
University of Bologna · ITIstituto Nazionale di Fisica Nucleare · ITGerman Research Centre for Artificial Intelligence · DE

Funding

European Space Agency (ESA) 4000123556
6 · The paper itself

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.

Indexed as

Deep LearningImage Processing, Computer-AssistedNeuronsCell NucleusMicroscopy, Fluorescence

Identifiers

PMID38341463
PMCPMC10858880
OpenAlexW4391722180

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.