Year: 2025
Type: article
Abstract: Reconstructing neuronal morphology from microscopy image stacks is essential for understanding brain function and behavior. While existing methods are capable of tracking neuronal tree structures and creating membrane surface meshes, they often lack seamless processing pipelines and suffer from stitching artifacts and reconstruction inconsistencies. In this study, we propose a new approach utilizing implicit neural representation to directly extract neuronal isosurfaces from raw image stacks by modeling signed distance functions (SDFs) with multi-layer perceptrons (MLPs). Our method accurately reconstructs the tubular, tree-like topology of neurons in complex spatial configurations, yielding highly precise neuronal membrane surface meshes. Extensive quantitative and qualitative evaluations across multiple datasets demonstrate the superior reliability of our approach compared to existing methods. The proposed method achieves a volumetric reconstruction accuracy of up to 98.2% and a volumetric IoU of 0.90.
Source: Mathematics
DOI: 10.3390/math13081276
Authors: Xiaoqiang Zhu, Yanhua Zhao, Lihua You
Institutions: Shanghai University, Bournemouth University
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4409408192