Probe-EM: Targeted Neuron Tracing via Training-Free Semantic Verification

• URL: http://arxiv.org/abs/2607.04696v1
• Date: Mon, 06 Jul 2026 05:56:26 GMT
• Title: Probe-EM: Targeted Neuron Tracing via Training-Free Semantic Verification
• Abstract summary: We propose a training-free, targeted neuron tracing framework for processing terabyte- or petabyte-scale electron microscopy data.<n>Specifically, we introduce a skeleton-guided Heuristic Spatial Search paradigm that leverages geometric priors to iteratively reconstruct neuronal morphologies.<n>We further develop a Dimension-Aware Semantic Verification strategy built upon the foundation model NeuroSAM 2.
• Score: 13.282533076475383
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Establishing large-scale, high-resolution neural connectivity maps is fundamental to elucidating the structural basis of brain function. However, when processing terabyte- or petabyte-scale electron microscopy data, over-segmentation inherent in automated reconstruction algorithms remains a critical bottleneck, requiring extensive manual proofreading spanning person-years. To alleviate the heavy reliance on annotated data and the limited flexibility of conventional tracing methods, we propose a training-free, targeted neuron tracing framework. Specifically, we introduce a skeleton-guided Heuristic Spatial Search paradigm that leverages geometric priors to iteratively reconstruct neuronal morphologies through a probing-verification cycle. To achieve robust zero-shot semantic verification, we further develop a Dimension-Aware Semantic Verification strategy built upon the foundation model NeuroSAM 2. This strategy resolves intra-slice splits via Planar Ensemble Consensus and inter-slice splits via Axial Spatio-Temporal Propagation. Notably, we integrate the proposed workflow into the Neuroglancer visualization platform, enabling an interactive human-in-the-loop proofreading system. Experimental results demonstrate that the proposed method outperforms supervised baselines and reduces manual proofreading time by 33.4%. The source code is publicly available at https://github.com/HeadLiuYun/Probe-EM.

Related papers

• Cross-Subject Semantic Decoding with Shared-Space Alignment for Generalized Neural Representation Learning [80.18561652862725]
  Generalizing across subjects remains challenging in invasive neural recordings.<n>We propose a cross-subject semantic decoding framework that aligns neural responses to speech perception from multiple subjects into a shared latent space.
  arXiv  Detail & Related papers  (2026-07-03T03:59:16Z)
• Functional Similarity Metric for Neural Networks: Overcoming Parametric Ambiguity via Activation Region Analysis [0.0]
  In ReLU networks, identical functional mappings can be achieved through entirely different weight configurations.<n>Traditional comparison methods exhibit extreme instability to slight weight perturbations during training.<n>This paper proposes a mathematically grounded approach to constructing a stable canonical representation of neural networks.
  arXiv  Detail & Related papers  (2026-04-04T17:50:07Z)
• Pixel Embedding Method for Tubular Neurite Segmentation [14.942761606240683]
  We introduce a deep network that outputs pixel level embedding vectors and design a corresponding loss function.<n>We also develop an end to end pipeline that directly maps raw neuronal images to SWC formatted neuron structure trees.<n> Experiments on our fMOST imaging dataset demonstrate that, compared to several classical methods, our approach significantly reduces the error rate in neuronal topology reconstruction.
  arXiv  Detail & Related papers  (2025-07-31T09:11:15Z)
• Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing [72.45257414889478]
  We aim to reduce human workload by predicting connectivity between over-segmented neuron pieces. We first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain. We propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding.
  arXiv  Detail & Related papers  (2024-01-05T19:45:12Z)
• Split-Boost Neural Networks [1.1549572298362787]
  We propose an innovative training strategy for feed-forward architectures - called split-boost. Such a novel approach ultimately allows us to avoid explicitly modeling the regularization term. The proposed strategy is tested on a real-world (anonymized) dataset within a benchmark medical insurance design problem.
  arXiv  Detail & Related papers  (2023-09-06T17:08:57Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• Estimation of the Mean Function of Functional Data via Deep Neural Networks [6.230751621285321]
  We propose a deep neural network method to perform nonparametric regression for functional data. The proposed method is applied to analyze positron emission tomography images of patients with Alzheimer disease.
  arXiv  Detail & Related papers  (2020-12-08T17:18:16Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Supervised Learning with First-to-Spike Decoding in Multilayer Spiking Neural Networks [0.0]
  We propose a new supervised learning method that can train multilayer spiking neural networks to solve classification problems. The proposed learning rule supports multiple spikes fired by hidden neurons, and yet is stable by relying on firstspike responses generated by a deterministic output layer. We also explore several distinct spike-based encoding strategies in order to form compact representations of input data.
  arXiv  Detail & Related papers  (2020-08-16T15:34:48Z)
• Parallelization Techniques for Verifying Neural Networks [52.917845265248744]
  We introduce an algorithm based on the verification problem in an iterative manner and explore two partitioning strategies. We also introduce a highly parallelizable pre-processing algorithm that uses the neuron activation phases to simplify the neural network verification problems.
  arXiv  Detail & Related papers  (2020-04-17T20:21:47Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.