PolypFlow: Reinforcing Polyp Segmentation with Flow-Driven Dynamics

2025-02-26

AI Summary

Colon polyp segmentation faces accuracy bottlenecks due to irregular morphologies, ambiguous boundaries, and imaging heterogeneity; existing U-Net variants lack explicit modeling of dynamic uncertainty evolution during segmentation. To address this, we propose the first physics-inspired framework embedding continuous flow dynamics into medical image segmentation: a flow matching–based, ODE-driven dynamic optimization mechanism that learns a velocity field to guide the initial prediction along an interpretable trajectory toward the ground-truth mask, enabling uncertainty-aware boundary refinement. Our method adopts a U-Net backbone and supports visualization of dynamic segmentation trajectories. Evaluated on multi-center datasets, it achieves state-of-the-art performance, demonstrating robustness under low-contrast conditions and motion artifacts—yielding a 3.2% Dice improvement and an 18.7% reduction in HD95.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Learning to Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Data transparency and provenance

Abstract

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 extbf{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.

Problem

• Polyp segmentation challenges due to irregular morphologies
• Lack of dynamic confidence modeling in U-Net variants
• Flow-driven dynamics enhance segmentation refinement robustness

Innovation

• Flow-driven dynamics
• ODE-based refinement
• Boundary-aware robustness