用合成数据训练模型，自动从脑血管显微图像中提取拓扑网络。
Synthetic Data for Blood Vessel Network Extraction
- 分三阶段生成逼真血管合成数据，融合生物约束与成像伪影。
- 仅用5个标注样本微调，边缘预测F1提升至0.626。
- 适合需要大规模血管拓扑分析的脑卒中研究者。
大脑血管网络在中风研究中至关重要，其拓扑结构对血流动力学分析具有决定性意义。然而，从显微图像中提取详细拓扑信息仍面临挑战，主要源于标注数据稀缺及对高拓扑精度的需求。本文结合合成数据生成与深度学习，实现从体积显微图像中自动提取血管网络图结构。为缓解数据稀缺问题，提出一个全流程合成数据生成管道：从抽象图生成，经血管掩码创建，到真实医学图像合成，各阶段均融入生物约束与成像伪影。基于该合成数据，构建两阶段3D U-Net模型进行节点检测与边预测。在真实显微数据上仅用5个手工标注样本微调后，边缘预测F1分数从0.496提升至0.626。结果表明，自动化血管网络提取已具备实际可行性，为中风研究中的大规模血管分析开辟新路径。
原文摘要 · Abstract (English)
Blood vessel networks in the brain play a crucial role in stroke research, where understanding their topology is essential for analyzing blood flow dynamics. However, extracting detailed topological vessel network information from microscopy data remains a significant challenge, mainly due to the scarcity of labeled training data and the need for high topological accuracy. This work combines synthetic data generation with deep learning to automatically extract vessel networks as graphs from volumetric microscopy data. To combat data scarcity, we introduce a comprehensive pipeline for generating large-scale synthetic datasets that mirror the characteristics of real vessel networks. Our three-stage approach progresses from abstract graph generation through vessel mask creation to realistic medical image synthesis, incorporating biological constraints and imaging artifacts at each stage. Using this synthetic data, we develop a two-stage deep learning pipeline of 3D U-Net-based models for node detection and edge prediction. Fine-tuning on real microscopy data shows promising adaptation, improving edge prediction F1 scores from 0.496 to 0.626 by training on merely 5 manually labeled samples. These results suggest that automated vessel network extraction is becoming practically feasible, opening new possibilities for large-scale vascular analysis in stroke research.
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