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 Lorenzo Rinaldi Antonio Zoccoli
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 in which rodent neuronal cells'
nuclei and cytoplasm are stained with diverse markers to highlight their
anatomical or functional characteristics. Alongside the images, we provide
ground-truth annotations for several learning tasks, including semantic
segmentation, object detection, and counting. The contribution is two-fold.
First, given the variety of annotations and their accessible formats, we
envision our work facilitating methodological advancements in computer vision
approaches for segmentation, detection, feature learning, 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. The data are available at:
https://amsacta.unibo.it/id/eprint/7347