NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentation

Abstract

Accurate 3D neuron segmentation in fluorescence microscopyis critical for neuroscience. However, the sparse and elongated morphol-ogy of neurons poses significant challenges to existing segmentation meth-ods. These methods struggle to preserve both local details and globaltopology, leading to fragmented results. To address this, we proposeNeuroRefiner, a multi-agent system that formalizes the human expertworkflow involving iterative global observation and local editing. Specifi-cally, NeuroRefiner comprises three collaborative agents dedicated to di-agnosing topological errors, generating correction instructions, and vali-dating refinement quality. To facilitate agent instruction-guided segmen-tation refinement, we propose TopoRefineNet, a dedicated 3D U-Net-based tool that leverages cross-modality feature fusion to generate refinedmasks. Through multi-round agent reasoning and voxel-level editing,NeuroRefiner produces topologically more accurate segmentations withenhanced interpretability. Experiments on the BigNeuron, CWMBS, andZBFWB datasets demonstrate that NeuroRefiner outperforms state-of-the-art methods, notably achieving a 3.02% improvement in F1 score onthe challenging ZBFWB dataset.