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Source note3 min read

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

Source

arXiv

arxiv.org/abs/2608.09636v1

Read the source

Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Haiyang Yan, Jinyue Guo, Yanchao Zhang, Bingqing Wang, Zhenchen Li, Jing Liu, Jiazheng Liu, Linlin Li, Hua Han
  • Venue: arXiv: cs.CV
  • Published: 2026-08-10
  • Collected: 2026-08-11
  • URL: http://arxiv.org/abs/2608.09636v1
  • PDF: https://arxiv.org/pdf/2608.09636v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

The study introduces a new method called NeuroRefiner for accurately segmenting neurons in 3D fluorescence microscopy images. The method overcomes challenges posed by the sparse and elongated morphology of neurons, which often leads to fragmented results with existing methods. NeuroRefiner is a multi-agent system that mimics human expert workflow involving iterative global observation and local editing. It consists of three collaborative agents: one for diagnosing topological errors, another for generating correction instructions, and the last for validating refinement quality. The method also proposes TopoRefineNet, a tool that uses cross-modality feature fusion to generate refined masks for segmentation refinement. Experiments on various datasets show that NeuroRefiner outperforms existing methods, particularly improving F1 score by 3.02% on a challenging dataset.

What the Document Says

  • The study presents NeuroRefiner, a new method for accurate 3D neuron segmentation in fluorescence microscopy.
  • NeuroRefiner addresses the challenge of preserving both local details and global topology in neuron segmentation due to their sparse and elongated morphology.
  • NeuroRefiner is a multi-agent system that replicates human expert workflow involving iterative global observation and local editing.
  • The system comprises three collaborative agents: one for diagnosing topological errors, another for generating correction instructions, and the last for validating refinement quality.
  • TopoRefineNet is a tool proposed within NeuroRefiner to facilitate instruction-guided segmentation refinement. It uses cross-modality feature fusion.
  • NeuroRefiner's multi-round agent reasoning and voxel-level editing produce topologically more accurate segmentations with enhanced interpretability.
  • Experiments on several datasets demonstrate that NeuroRefiner outperforms state-of-the-art methods, notably improving F1 score by 3.02% on the challenging ZBFWB dataset.

Key Concepts Mentioned

  • Neuron segmentation
  • Fluorescence microscopy
  • Multi-agent system
  • Topological errors
  • Correction instructions
  • Cross-modality feature fusion
  • F1 score

Claims Made by the Document

  • NeuroRefiner accurately segments neurons in 3D fluorescence microscopy images.
  • NeuroRefiner overcomes challenges posed by sparse and elongated neuron morphology.
  • NeuroRefiner outperforms state-of-the-art methods.

Evidence Used

  • Results from experiments on various datasets

Methods / Approach

  • Iterative global observation and local editing
  • Three collaborative agents: topological error diagnosis, correction instruction generation, and refinement quality validation
  • TopoRefineNet for instruction-guided segmentation refinement using cross-modality feature fusion

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The text does not provide information on potential limitations of the NeuroRefiner method.

Open Questions

  • What specific datasets were used in the experiments?
  • What is the exact improvement in accuracy or other metrics across all tested datasets?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text mentions multiple datasets (BigNeuron, CWMBS, and ZBFWB) but does not specify which ones were used for specific tests. Also, while it states an F1 score improvement on the challenging ZBFWB dataset, it doesn't provide details about other datasets.

Raw Abstract / Excerpt

Accurate 3D neuron segmentation in fluorescence microscopy is critical for neuroscience. However, the sparse and elongated morphology of neurons poses significant challenges to existing segmentation methods. These methods struggle to preserve both local details and global topology, leading to fragmented results. To address this, we propose NeuroRefiner, a multi-agent system that formalizes the human expert workflow involving iterative global observation and local editing. Specifically, NeuroRefiner comprises three collaborative agents dedicated to diagnosing topological errors, generating correction instructions, and validating refinement quality. To facilitate agent instruction-guided segmentation refinement, we propose TopoRefineNet, a dedicated 3D U-Net-based tool that leverages cross-modality feature fusion to generate refined masks. Through multi-round agent reasoning and voxel-level editing, NeuroRefiner produces topologically more accurate segmentations with enhanced interpretability. Experiments on the BigNeuron, CWMBS, and ZBFWB datasets demonstrate that NeuroRefiner outperforms state-of-the-art methods, notably achieving a 3.02% improvement in F1 score on the challenging ZBFWB dataset.