针对超声图像边界模糊问题，提出频域引导的分割框架提升精度与泛化性。
FreqDINO: Frequency-Guided Adaptation for Generalized Boundary-Aware Ultrasound Image Segmentation
- 分离低频结构与多尺度高频边界，通过可学习注意力对齐
- 利用高频成分提取边界原型并优化空间特征，提升边缘清晰度
- 适合需要高精度边界分割的临床超声分析场景
超声图像分割对临床诊断至关重要，但受斑点噪声和成像伪影挑战。尽管DINOv3在医学图像分割中展现出强大表征能力，但其预训练于自然图像，缺乏对超声特有边界退化的敏感性。为此，我们提出频域引导的分割框架FreqDINO，以增强边界感知与结构一致性。具体地，设计多尺度频域提取与对齐（MFEA）策略，分离低频结构与多尺度高频边界细节，并通过可学习注意力对齐；引入频域引导的边界精炼（FGBR）模块，从高频成分中提取边界原型并精炼空间特征；同时设计多任务边界引导解码器（MBGD），确保边界与语义预测的空间一致性。大量实验表明，FreqDINO超越现有方法，具备优异的泛化能力。代码已公开于https://github.com/MingLang-FD/FreqDINO。
原文摘要 · Abstract (English)
Ultrasound image segmentation is pivotal for clinical diagnosis, yet challenged by speckle noise and imaging artifacts. Recently, DINOv3 has shown remarkable promise in medical image segmentation with its powerful representation capabilities. However, DINOv3, pre-trained on natural images, lacks sensitivity to ultrasound-specific boundary degradation. To address this limitation, we propose FreqDINO, a frequency-guided segmentation framework that enhances boundary perception and structural consistency. Specifically, we devise a Multi-scale Frequency Extraction and Alignment (MFEA) strategy to separate low-frequency structures and multi-scale high-frequency boundary details, and align them via learnable attention. We also introduce a Frequency-Guided Boundary Refinement (FGBR) module that extracts boundary prototypes from high-frequency components and refines spatial features. Furthermore, we design a Multi-task Boundary-Guided Decoder (MBGD) to ensure spatial coherence between boundary and semantic predictions. Extensive experiments demonstrate that FreqDINO surpasses state-of-the-art methods with superior achieves remarkable generalization capability. The code is at https://github.com/MingLang-FD/FreqDINO.
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