Fluorescent Neuronal Cells v2

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 Science 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 envision our work would facilitate 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 would catalyze breakthroughs in fluorescence microscopy analysis and promote cutting-edge discoveries in life sciences.

For more information, please refer to Clissa, L. et al., 2024. Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy. Scientific data. https://doi.org/10.1038/s41597-024-03005-9.

This research was partly funded by PNRR - M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 - “FAIR - Future Artificial Intelligence Research” - Spoke 8 “Pervasive AI” and the European Commission under the NextGeneration EU programme.
The collection of original images was supported by funding from the University of Bologna and the European Space Agency (Research agreement collaboration 4000123556).

荧光神经元细胞 v2（Fluorescent Neuronal Cells v2）是一套荧光显微镜图像及对应真值标注集，旨在推动生命科学与深度学习领域的创新性研究。

本数据集包含三组图像集合，其中啮齿类动物的神经元细胞核与细胞质通过多种标记物染色，以凸显其解剖学或功能特性。

具体而言，本次发布共包含1874张高分辨率图像，以及750组对应真值标注，可用于语义分割、目标检测与计数等多项学习任务。

本数据集的贡献主要体现在两个方面：其一，得益于丰富多样的标注类型与便捷的标注格式，我们期望本工作能够推动计算机视觉相关方法的方法论进步，涵盖分割、检测、特征学习、无监督与自监督学习、迁移学习及其他相关领域；其二，通过支持大规模探索与基准测试，我们希望荧光神经元细胞 v2能够助力荧光显微镜图像分析领域取得突破，并推动生命科学领域的前沿发现。

如需了解更多信息，请参阅Clissa L.等人2024年发表于《科学数据（Scientific Data）》的论文《荧光神经元细胞 v2：面向显微镜深度学习的多任务、多格式标注》，链接：https://doi.org/10.1038/s41597-024-03005-9。

本研究部分受意大利国家恢复与韧性计划（PNRR）M4C2投资项目1.3、编号为PE00000013的“FAIR——未来人工智能研究”扩展伙伴关系第8分支“普适人工智能”，以及欧盟委员会“下一代欧盟（NextGeneration EU）”计划资助。原始图像的采集工作得到了博洛尼亚大学与欧洲空间局（研究合作协议编号4000123556）的经费支持。

搜集汇总

背景与挑战

背景概述

Fluorescent Neuronal Cells v2是一个包含1874张高分辨率荧光显微镜图像和750个对应标注的数据集，主要用于语义分割、目标检测和计数等深度学习任务。该数据集旨在推动生命科学和计算机视觉领域的研究，特别是荧光显微镜图像分析方面的进展。