Abstract
Accurate segmentation of subcutaneous vessels from clinical images is hampered by scarce, expensive ground truth and by low contrast, noisy appearance of vessels across patients and modalities. We present a novel weakly supervised training framework tailored for subcutaneous vessel segmentation that leverages inexpensive sparse annotations (e.g., centerline traces, dot markers, or short scribbles). Sparse labels are expanded into dense, probabilistic supervision via a differentiable random walk label propagation model whose transition weights incorporate image driven vesselness cues and tubular continuity priors. The propagation yields per-pixel hitting probabilities together with calibrated uncertainty estimates; these are incorporated into an uncertainty weighted loss to avoid over fitting to ambiguous regions. Crucially, the label-propagator is learned jointly with a CNN based segmentation predictor, enabling the system to discover vessel edges and continuity constraints without explicit edge supervision. We further introduce a topology aware regularizer that encourages centerline connectivity and penalizes spurious branches, improving clinical usability. In experiments on clinical subcutaneous imaging datasets, our method consistently outperforms naive training on sparse labels and conventional dense pseudo-labeling, producing more complete vascular maps and better calibrated uncertainty for downstream decision making. The approach substantially reduces annotation burden while preserving clinically relevant vessel topology.
Abstract (translated)
从临床图像中精确分割皮下血管受到稀缺且昂贵的真值标注和患者间及模态间的低对比度、噪声外观的影响。我们提出了一种新颖的弱监督训练框架，专门用于皮下血管分割，该框架利用廉价稀疏注释（例如，中心线轨迹、点标记或简短涂鸦）。这些稀疏标签通过一种可微分随机游走标签传播模型扩展为密集且概率化的监督信号，该模型在传输权重中整合了图像驱动的血管特征和管状连续性先验。这种传播产生了每个像素的命中概率以及校准过的不确定性估计；这些信息被纳入一个不确定性加权损失函数中以避免过度拟合到模棱两可区域。关键在于标签传播器与基于 CNN 的分割预测器共同学习，从而使系统能够发现血管边缘和连续性约束而无需明确的边缘监督。此外，我们引入了一种拓扑感知正则化器，鼓励中心线连通并惩罚虚假分支，从而提高临床实用性。在对临床皮下成像数据集进行实验时，我们的方法始终优于基于稀疏标签的简单训练以及传统的密集伪标注，生成了更完整的血管图，并且为下游决策提供了更好的校准不确定性估计。该方法大幅减少了注释负担，同时保留了临床上相关的血管拓扑结构。
URL
https://arxiv.org/abs/2508.06819
