用扩散模型同时生成专家共识和个体偏好，解决医学影像分割标注差异问题。
DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model
- 分两阶段设计：先建群体共识，再通过自适应提示捕捉专家个性偏好
- 在LIDC-IDRI和NPC-170数据集上所有指标均优于现有最先进方法
- 适合需要兼顾一致性和个性化判断的医学影像分析场景
标注变异性仍是医学图像分割中的重大挑战，源于成像边界模糊及临床经验差异。传统深度学习方法生成单一确定性分割结果，难以捕捉标注者偏差。尽管已有研究探索多标注者分割，但多数方法仅聚焦单一视角——或生成概率性‘金标准’共识，或保留专家个体偏好，难以提供全面视图。本文提出DiffOSeg，一种基于扩散模型的两阶段框架，旨在同时实现共识驱动（融合所有专家意见）与偏好驱动（反映专家个体评估）。第一阶段通过概率共识策略建立群体共识，第二阶段利用自适应提示捕捉专家特定偏好。在两个公开数据集LIDC-IDRI和NPC-170上的实验表明，该模型在所有评估指标上均优于现有最先进方法。源代码已开源：https://github.com/string-ellipses/DiffOSeg。
原文摘要 · Abstract (English)
Annotation variability remains a substantial challenge in medical image segmentation, stemming from ambiguous imaging boundaries and diverse clinical expertise. Traditional deep learning methods producing single deterministic segmentation predictions often fail to capture these annotator biases. Although recent studies have explored multi-rater segmentation, existing methods typically focus on a single perspective -- either generating a probabilistic ``gold standard'' consensus or preserving expert-specific preferences -- thus struggling to provide a more omni view. In this study, we propose DiffOSeg, a two-stage diffusion-based framework, which aims to simultaneously achieve both consensus-driven (combining all experts' opinions) and preference-driven (reflecting experts' individual assessments) segmentation. Stage I establishes population consensus through a probabilistic consensus strategy, while Stage II captures expert-specific preference via adaptive prompts. Demonstrated on two public datasets (LIDC-IDRI and NPC-170), our model outperforms existing state-of-the-art methods across all evaluated metrics. Source code is available at https://github.com/string-ellipses/DiffOSeg .
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