MedFlowSeg: Flow Matching for Medical Image Segmentation with Frequency-Aware Attention

2026-04-21

AI Summary

This work addresses the high computational cost, reliance on iterative sampling, and architectural constraints of U-Net-based diffusion models in medical image segmentation by introducing conditional flow matching to this task for the first time. The proposed method learns a time-dependent vector field that enables a deterministic, single-step mapping from a simple prior distribution to the target segmentation distribution. It innovatively incorporates a dual-conditioning mechanism, a dual-branch spatial attention module, and a frequency-aware attention module to effectively integrate multi-scale structural and spectral information while enabling cross-domain feature modulation. Evaluated across multiple medical imaging modalities, the approach significantly outperforms existing diffusion-based methods, achieving high segmentation accuracy with substantially reduced inference costs.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multimodal Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Flow matching has recently emerged as a principled framework for learning continuous-time transport maps, enabling efficient deterministic generation without relying on stochastic diffusion processes. While generative modeling has shown promise for medical image segmentation, particularly in capturing uncertainty and complex anatomical variability, existing approaches are predominantly built upon diffusion models, which incur substantial computational overhead due to iterative sampling and are often constrained by UNet-based parameterizations. In this work, we introduce MedFlowSeg, a conditional flow matching framework that formulates medical image segmentation as learning a time-dependent vector field that transports a simple prior distribution to the target segmentation distribution. This formulation enables one-step deterministic inference while preserving the expressiveness of generative modeling. We further develop a dual-conditioning mechanism to incorporate structured priors into the learned flow. Specifically, we propose a Dual-Branch Spatial Attention module that injects multi-scale structural information into the flow field, and a Frequency-Aware Attention module that models cross-domain interactions between spatial and spectral representations via discrepancy-aware fusion and time-dependent modulation. Together, these components provide an effective parameterization of conditional flows that capture both global anatomical structure and fine-grained boundary details. We provide extensive empirical validation across multiple medical imaging modalities, demonstrating that MedFlowSeg achieves state-of-the-art performance while significantly reducing computational cost compared to diffusion-based methods. Our results highlight the potential of flow matching as a theoretically grounded and computationally efficient alternative for generative medical image segmentation.

Problem

• medical image segmentation
• diffusion models
• computational overhead
• anatomical variability
• boundary details

Innovation

• flow matching
• medical image segmentation
• frequency-aware attention
• dual-conditioning
• deterministic generation