Ambiguity-aware Truncated Flow Matching for Ambiguous Medical Image Segmentation

• URL: http://arxiv.org/abs/2511.06857v1
• Date: Mon, 10 Nov 2025 08:57:06 GMT
• Title: Ambiguity-aware Truncated Flow Matching for Ambiguous Medical Image Segmentation
• Abstract summary: We propose Ambiguity-aware Truncated Flow Matching (ATFM) to enhance accuracy and diversity of predictions.<n>GTR is introduced to enhance both fidelity of predictions and reliability of truncation distribution.<n>SFM is proposed to enhance the plausibility of diverse predictions by extending semantic-aware flow transformation.<n>ATFM improves GED and HM-IoU by up to $12%$ and $7.3%$ compared to advanced methods.
• Score: 10.578008836960134
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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