VessShape: Few-shot 2D blood vessel segmentation by leveraging shape priors from synthetic images

TL;DR

VessShape introduces a synthetic dataset generation approach that emphasizes vessel shape priors, enabling robust few-shot and zero-shot blood vessel segmentation across different medical imaging domains.

Contribution

The paper presents a novel synthetic data generation method to embed shape priors into segmentation models, improving generalization and data efficiency in blood vessel segmentation tasks.

Findings

Pre-trained models perform well with only 4-10 samples for fine-tuning.

Models exhibit zero-shot segmentation capabilities in unseen domains.

Shape bias enhances robustness and generalization in medical image segmentation.

Abstract

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.