Lightweight Deep Learning Framework for Astrocyte-Centric 3D Reconstruction across Normal and Stress-associated Ultrastructural States

Abstract

The morphology of astrocytes is closely associated with their ability to support and modulate neural circuit functions; however, its reconstruction from volume electron microscopy (vEM) data remains labor-intensive and computationally resource-intensive, placing substantial barriers on its broader adoption and scalability despite the high spatial resolution of vEM. We present AstroSeg-vEM, a lightweight, astrocyte-centric framework for morphology-preserving astrocyte segmentation, 3D reconstruction, and quantitative morphological analysis in vEM images. AstroSeg-vEM combines transformer-based 2D segmentation with dedicated post-processing methods and operates directly on high-resolution EM sections, making it well suited to anisotropic vEM data and downstream proofreading workflows while preserving the full in-plane resolution of the original EM data. Across four mouse hippocampal datasets representing normal and morphologically defined stress-associated astrocyte ultrastructural states, AstroSeg-vEM demonstrates robust performance and generalization, achieving average Dice and weighted Dice values of 0.92 and 0.95, respectively, on a held-out test dataset. Its lightweight architecture enables efficient, high-fidelity reconstruction on consumer-grade hardware, providing a scalable and accessible platform for large-scale astrocyte ultrastructure analysis and future astrocyte-neuron interaction studies.

arXiv commentAccepted for publication in npj Imaging. 19 pages, 4 figures, 6 tables, including Supplementary Material. Code is available at https://github.com/QuIN-Lab/AstroSeg-vEM