用自然语言提示实现医学影像分割的个性化，让模型像专家一样灵活调整
ProSona: Prompt-Guided Personalization for Multi-Expert Medical Image Segmentation
- 通过潜空间建模专家标注风格，用提示词控制分割结果
- 在肺结节和前列腺MRI数据上，分割精度提升1%以上，误差降低17%
- 适合需要个性化医疗分析的研究者和临床医生使用
自动化医学图像分割面临高观察者间差异问题，尤其在肺结节勾画任务中专家意见常不一致。现有方法或融合为单一共识掩码，或为每位标注者设置独立模型分支。我们提出ProSona，一种两阶段框架，学习标注风格的连续潜空间，支持通过自然语言提示进行可控个性化。采用概率U-Net主干捕捉多样专家假设，结合提示引导投影机制在潜空间中生成个性化分割结果。多层级对比目标对齐文本与视觉表征，促进专家风格的解耦与可解释性。在LIDC-IDRI肺结节和多机构前列腺MRI数据集上，ProSona相比DPersona将广义能量距离降低17%，平均Dice提升超过1个百分点。结果表明，自然语言提示可提供灵活、准确且可解释的个性化分割控制。代码已公开。
原文摘要 · Abstract (English)
Automated medical image segmentation suffers from high inter-observer variability, particularly in tasks such as lung nodule delineation, where experts often disagree. Existing approaches either collapse this variability into a consensus mask or rely on separate model branches for each annotator. We introduce ProSona, a two-stage framework that learns a continuous latent space of annotation styles, enabling controllable personalization via natural language prompts. A probabilistic U-Net backbone captures diverse expert hypotheses, while a prompt-guided projection mechanism navigates this latent space to generate personalized segmentations. A multi-level contrastive objective aligns textual and visual representations, promoting disentangled and interpretable expert styles. Across the LIDC-IDRI lung nodule and multi-institutional prostate MRI datasets, ProSona reduces the Generalized Energy Distance by 17% and improves mean Dice by more than one point compared with DPersona. These results demonstrate that natural-language prompts can provide flexible, accurate, and interpretable control over personalized medical image segmentation. Our implementation is available online 1 .
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