Text-guided few-shot liver and tumor segmentation.

Introduction: High-precision liver and tumor segmentation is a cornerstone of digital oncology, yet its clinical deployment remains constrained by two persistent challenges: the scarcity of pixel-level annotations and severe performance degradation under cross-center domain shift. Although few shot learning offers a promising direction for data-efficient modeling, existing approaches relying solely on visual similarity often fail to generalize across heterogeneous clinical environments. Method: In this study, we propose a text-guided few-shot segmentation framework that integrates clinical semantic information as an explicit inductive bias to bridge the gap between low-level pixels and high-level diagnostic reasoning. The framework consists of three synergistic modules: an Automated Semantic Generator that employs a large-scale vision-language model to encode clinically structured semantic descriptions into structured linguistic priors. A Text-Guided Gating (TGG) mechanism that adaptively modulates visual representations to filter scanner-dependent artifacts, and a Decoupled Prototype Learner that constructs unbiased class prototypes via per-image averaging and gradient detachment to address extreme class imbalance. Together, these components transform segmentation from a pure visual matching task into a knowledge guided reasoning process, ensuring both data efficiency and improved cross-dataset robustness across heterogeneous datasets. Results: Experiments on LiTS and 3DIRCADb show that the method consistently outperforms state-of-the-art supervised, foundation-model-based, and few-shot baselines. On 3DIRCADb, compared to PANet, the strongest few-shot baseline, our method improves external liver Dice by 8.7 percentage points and external tumor Dice by 26.3 percentage points, effectively mitigating performance degradation in conventional supervised models. Discussion: These results demonstrate that cross-modal semantic guidance enhances robust medical image segmentation under domain shift.