用流动动力学优化肠镜图像息肉分割，提升边界精度与鲁棒性。
PolypFlow: Reinforcing Polyp Segmentation with Flow-Driven Dynamics
- 通过求解微分方程，逐步修正初始分割结果，实现可解释的迭代优化。
- 在多个数据集上达到最优性能，光照变化下仍保持稳定表现。
- 适合需要高精度与可解释性的医学图像分割场景。
准确分割息肉仍具挑战，原因在于病灶形态不规则、边界模糊及成像条件多样。尽管U-Net变体擅长局部特征融合，但缺乏对不确定性下分割置信度动态演变的显式建模。受流模型可解释性的启发，我们提出PolypFlow，一种引入物理启发优化动力学的分割增强架构。与传统级联网络不同，该框架通过求解常微分方程（ODE），利用学习到的速度场逐步将粗略预测对齐真实标签。这种基于轨迹的精修带来两大优势：1）可解释优化：中间流步骤可视化模型如何修正欠分割区域并锐化边界，揭示“黑箱”精修过程；2）边界感知鲁棒性：流动力学显式建模息肉边缘梯度方向，增强对低对比度区域和运动伪影的抗性。大量实验表明，PolypFlow在多个数据集上达到当前最优性能，且在不同光照条件下保持一致表现。
原文摘要 · Abstract (English)
Accurate polyp segmentation remains challenging due to irregular lesion morphologies, ambiguous boundaries, and heterogeneous imaging conditions. While U-Net variants excel at local feature fusion, they often lack explicit mechanisms to model the dynamic evolution of segmentation confidence under uncertainty. Inspired by the interpretable nature of flow-based models, we present \textbf{PolypFLow}, a flow-matching enhanced architecture that injects physics-inspired optimization dynamics into segmentation refinement. Unlike conventional cascaded networks, our framework solves an ordinary differential equation (ODE) to progressively align coarse initial predictions with ground truth masks through learned velocity fields. This trajectory-based refinement offers two key advantages: 1) Interpretable Optimization: Intermediate flow steps visualize how the model corrects under-segmented regions and sharpens boundaries at each ODE-solver iteration, demystifying the ``black-box" refinement process; 2) Boundary-Aware Robustness: The flow dynamics explicitly model gradient directions along polyp edges, enhancing resilience to low-contrast regions and motion artifacts. Numerous experimental results show that PolypFLow achieves a state-of-the-art while maintaining consistent performance in different lighting scenarios.
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