A simultaneous enhancement of accuracy and diversity of predictions remains a challenge in ambiguous medical image segmentation (AMIS) due to the inherent trade-offs. While truncated diffusion probabilistic models (TDPMs) hold strong potential with a paradigm optimization, existing TDPMs suffer from entangled accuracy and diversity of predictions with insufficient fidelity and plausibility. To address the aforementioned challenges, we propose Ambiguity-aware Truncated Flow Matching (ATFM), which introduces a novel inference paradigm and dedicated model components. Firstly, we propose Data-Hierarchical Inference, a redefinition of AMIS-specific inference paradigm, which enhances accuracy and diversity at data-distribution and data-sample level, respectively, for an effective disentanglement. Secondly, Gaussian Truncation Representation (GTR) is introduced to enhance both fidelity of predictions and reliability of truncation distribution, by explicitly modeling it as a Gaussian distribution at $T_{\text{trunc}}$ instead of using sampling-based approximations.Thirdly, Segmentation Flow Matching (SFM) is proposed to enhance the plausibility of diverse predictions by extending semantic-aware flow transformation in Flow Matching (FM). Comprehensive evaluations on LIDC and ISIC3 datasets demonstrate that ATFM outperforms SOTA methods and simultaneously achieves a more efficient inference. ATFM improves GED and HM-IoU by up to $12\%$ and $7.3\%$ compared to advanced methods.

翻译：在模糊医学图像分割（AMIS）中，由于固有的权衡关系，同时提升预测的准确性与多样性仍是一项挑战。尽管截断扩散概率模型（TDPMs）通过范式优化展现出强大潜力，但现有TDPMs存在预测准确性与多样性相互纠缠的问题，且保真度与合理性不足。为应对上述挑战，本文提出模糊感知截断流匹配（ATFM），引入了一种新颖的推理范式及专用模型组件。首先，我们提出数据分层推理，这是一种针对AMIS特定推理范式的重新定义，分别在数据分布层面和数据样本层面增强准确性与多样性，从而实现有效的解耦。其次，引入高斯截断表示（GTR），通过在$T_{\text{trunc}}$处将其显式建模为高斯分布而非基于采样的近似，以提升预测的保真度与截断分布的可靠性。第三，提出分割流匹配（SFM），通过扩展流匹配（FM）中的语义感知流变换，以增强多样化预测的合理性。在LIDC和ISIC3数据集上的综合评估表明，ATFM优于现有最先进方法，并同时实现了更高效的推理。与先进方法相比，ATFM将GED和HM-IoU分别提升了最高$12\\%$和$7.3\\%。