Fluorescence Microscopy Image Datasets for Deep Learning Segmentation of Intracellular Orgenelle Networks

Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures. To support the training and testing of deep learning segmentation models, we construct two fluorescence image datasets, ER and MITO, for the ER network and the mitochondrial network, respectively. We provide manual segmentation for these organelle networks in binary masks. The datasets have been used to evaluate and compare performance of the methods proposed in our article “Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks”.

内质网（endoplasmic reticulum, ER）网络与线粒体网络（mitochondrial network）等细胞内细胞器网络，均发挥着至关重要的生理功能。此类网络的形态结构，对其生理功能的实现起着关键调控作用。为通过分析此类网络的形态结构以支撑疾病诊断、药物研发等应用场景，精准的图像分割是必不可少的前提条件。深度学习模型在这类复杂网络结构的精准、鲁棒分割任务中已展现出显著优势。为支撑深度学习分割模型的训练与测试工作，我们分别针对内质网网络与线粒体网络，构建了两类荧光图像数据集：ER数据集与MITO数据集。我们为这些细胞器网络提供了二值掩码（binary masks）形式的手动分割标注结果。本数据集已用于评估与对比我们在论文《Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks》中提出的方法的性能。