Pixel Embedding Method for Tubular Neurite Segmentation

2025-07-31

AI Summary

To address the challenges of large-scale neuronal segmentation and topological reconstruction arising from complex dendritic branching morphologies and severe fiber occlusion in volumetric neuroimaging data, this paper proposes an end-to-end deep learning framework that directly generates SWC-formatted structural trees from raw images. Methodologically, it introduces pixel-wise embedding vectors coupled with a customized contrastive loss function to explicitly model local connectivity, thereby effectively disentangling adjacent fibers within occluded regions. Additionally, a novel topology-aware evaluation metric is designed to more accurately quantify reconstruction fidelity. Experiments on the fMOST dataset demonstrate that our approach significantly reduces both branch misconnections and discontinuities, achieving superior performance over multiple state-of-the-art methods in both segmentation accuracy and topological preservation.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Structured Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Automatic segmentation of neuronal topology is critical for handling large scale neuroimaging data, as it can greatly accelerate neuron annotation and analysis. However, the intricate morphology of neuronal branches and the occlusions among fibers pose significant challenges for deep learning based segmentation. To address these issues, we propose an improved framework: First, we introduce a deep network that outputs pixel level embedding vectors and design a corresponding loss function, enabling the learned features to effectively distinguish different neuronal connections within occluded regions. Second, building on this model, we develop an end to end pipeline that directly maps raw neuronal images to SWC formatted neuron structure trees. Finally, recognizing that existing evaluation metrics fail to fully capture segmentation accuracy, we propose a novel topological assessment metric to more appropriately quantify the quality of neuron segmentation and reconstruction. Experiments on our fMOST imaging dataset demonstrate that, compared to several classical methods, our approach significantly reduces the error rate in neuronal topology reconstruction.

Problem

• Segmenting intricate neuronal branches with occlusions
• Mapping raw images to SWC neuron structure trees
• Improving topological evaluation metrics for segmentation

Innovation

• Deep network outputs pixel level embedding vectors
• End to end pipeline maps images to SWC trees
• Novel topological metric quantifies segmentation quality