Semantic segmentation of blood vessels is an important task in medical image analysis, but its progress is often hindered by the scarcity of large annotated datasets and the poor generalization of models across different imaging modalities. A key aspect is the tendency of Convolutional Neural Networks (CNNs) to learn texture-based features, which limits their performance when applied to new domains with different visual characteristics. We hypothesize that leveraging geometric priors of vessel shapes, such as their tubular and branching nature, can lead to more robust and data-efficient models. To investigate this, we introduce VessShape, a methodology for generating large-scale 2D synthetic datasets designed to instill a shape bias in segmentation models. VessShape images contain procedurally generated tubular geometries combined with a wide variety of foreground and background textures, encouraging models to learn shape cues rather than textures. We demonstrate that a model pre-trained on VessShape images achieves strong few-shot segmentation performance on two real-world datasets from different domains, requiring only four to ten samples for fine-tuning. Furthermore, the model exhibits notable zero-shot capabilities, effectively segmenting vessels in unseen domains without any target-specific training. Our results indicate that pre-training with a strong shape bias can be an effective strategy to overcome data scarcity and improve model generalization in blood vessel segmentation.

翻译：血管的语义分割是医学图像分析中的重要任务，但其进展常受限于大规模标注数据集的稀缺以及模型在不同成像模态间泛化能力不足的问题。一个关键因素在于卷积神经网络（CNNs）倾向于学习基于纹理的特征，这限制了其在具有不同视觉特征的新领域中的应用性能。我们假设，利用血管形状的几何先验（如管状和分支结构特性）可以构建更鲁棒且数据高效的模型。为验证这一假设，我们提出了VessShape，一种用于生成大规模二维合成数据集的方法，旨在向分割模型注入形状偏置。VessShape图像包含程序化生成的管状几何结构，并结合多样化的前景与背景纹理，促使模型学习形状线索而非纹理特征。实验表明，在VessShape图像上预训练的模型在两个不同领域的真实数据集上实现了优异的少样本分割性能，仅需4至10个样本进行微调。此外，该模型展现出显著的零样本能力，无需任何目标领域特定训练即可有效分割未见领域中的血管。我们的结果表明，通过强形状偏置进行预训练是克服数据稀缺性、提升血管分割模型泛化能力的有效策略。