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”.

细胞内细胞器网络（如内质网（endoplasmic reticulum, ER）网络与线粒体网络）具备至关重要的生理功能。此类网络的形态特征在其功能调控中扮演着核心角色。针对该类网络的形态学分析离不开精准的图像分割技术，其应用场景涵盖疾病诊断、药物研发等多个领域。深度学习模型在这类复杂网络结构的高精度、高鲁棒性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，我们分别构建了面向内质网网络与线粒体网络的两类荧光图像数据集——ER数据集与MITO数据集，并为这些细胞器网络提供了手动标注的二值掩码分割结果。本数据集已被用于评估与对比我们发表于论文"Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks"中所提出的各类方法的性能。