MOTIVATION: Morphological analysis of dendritic spines is critical to understanding the function and dysfunction of neural circuits. The growing trends of the large-scale electron microscopy (EM) imaging systems and automatic cellular reconstruction provide unprecedented opportunities to investigate the ultrastructure of dendrites. This morphometric analysis of dendritic spines requires accurate compartment segmentation methods as well as meaningful quantification methods. However, most existing methods rely on surface or volumetric information alone, which may not deliver accurate segmentation results. RESULTS: We developed VSOT, a method based on Volume-Surface Optimization, designed for the accurate structural analysis of dendritic reconstruction. VSOT accurately segments dendritic reconstructions into compartments, including spine, spine head, and spine neck, by leveraging advanced optimization techniques that integrate local surface and global volumetric information. Our tests on public datasets of spine segmentation, as well as on a first-of-its-kind dataset of head-neck segmentation that we manually constructed, show that VSOT offers more accurate results than peer methods. When applied to a large EM dataset of different brain layers, VSOT reveals how the structure of dendrites varies across brain areas. Furthermore, we explored the structural relationships between neurons and astrocytes at tripartite synapses. With the newly developed computation methods, neuroscientists can exploit the large-scale volumetric EM data to address various scientific questions and advance the understanding of neural circuits. AVAILABILITY AND IMPLEMENTATION: VSOT is available at https://github.com/yu-lab-vt/VSOT. The data and codes in this study are available at Zenodo (https://doi.org/10.5281/zenodo.15115542).