This dataset includes 70 sets of 3D confocal high-resolution images. All images were imaged using an LSM800 Zeiss microscope with a Plan-apochromat 1.40-NA, 63× objective, and Zeiss ZEN Blue 2.6 software was used to acquire the images. Three channels were used to acquire transmitted light (TL), SYBR GoldTM- (Thermo Fisher Scientific, Inc.) labeled (nuclear and mitochondrial DNA), and TMRM-labeled (mitochondria) images. Each confocal image consists of 32 slices with an interval of 0.15 µm and a YX resolution of 917 × 917 pixels.


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.