Neuronal Mesh Reconstruction from Image Stacks Using Implicit Neural Representations

Xiaoqiang Zhu (), Yanhua Zhao and Lihua You
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Xiaoqiang Zhu: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Yanhua Zhao: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Lihua You: National Center for Computer Animation, Bournemouth University, Poole BH12 5BB, UK

Mathematics, 2025, vol. 13, issue 8, 1-19

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.

Keywords: implicit neural representations; SDF; deep learning; neuronal morphology; representation learning; neuron segmentation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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