Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy

TL;DR

Fluorescent Neuronal Cells v2 provides a comprehensive microscopy dataset with multi-task annotations to advance deep learning methods in biological image analysis and facilitate breakthroughs in neuroscience research.

Contribution

The dataset offers diverse annotations and formats, enabling methodological innovations and benchmarking in microscopy image analysis.

Findings

Supports segmentation, detection, and counting tasks

Facilitates benchmarking and method development

Aims to catalyze scientific discoveries in life sciences

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 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