Deep learning-based automatic medical image segmentation plays a critical role in clinical diagnosis and treatment planning but remains challenging in few-shot scenarios due to the scarcity of annotated training data. Recently, self-supervised foundation models such as DINOv3, which were trained on large natural image datasets, have shown strong potential for dense feature extraction that can help with the few-shot learning challenge. Yet, their direct application to medical images is hindered by domain differences. In this work, we propose DINO-AugSeg, a novel framework that leverages DINOv3 features to address the few-shot medical image segmentation challenge. Specifically, we introduce WT-Aug, a wavelet-based feature-level augmentation module that enriches the diversity of DINOv3-extracted features by perturbing frequency components, and CG-Fuse, a contextual information-guided fusion module that exploits cross-attention to integrate semantic-rich low-resolution features with spatially detailed high-resolution features. Extensive experiments on six public benchmarks spanning five imaging modalities, including MRI, CT, ultrasound, endoscopy, and dermoscopy, demonstrate that DINO-AugSeg consistently outperforms existing methods under limited-sample conditions. The results highlight the effectiveness of incorporating wavelet-domain augmentation and contextual fusion for robust feature representation, suggesting DINO-AugSeg as a promising direction for advancing few-shot medical image segmentation. Code and data will be made available on https://github.com/apple1986/DINO-AugSeg.

翻译：基于深度学习的自动医学图像分割在临床诊断与治疗规划中起着关键作用，但由于标注训练数据的稀缺，在少样本场景下仍面临挑战。近期，在大型自然图像数据集上训练的自监督基础模型（如DINOv3）已展现出强大的密集特征提取能力，有助于应对少样本学习难题。然而，领域差异阻碍了其直接应用于医学图像。本文提出DINO-AugSeg，一种利用DINOv3特征解决少样本医学图像分割挑战的新框架。具体而言，我们引入WT-Aug——一个基于小波的特征级增强模块，通过扰动频率分量来丰富DINOv3提取特征的多样性；以及CG-Fuse——一个上下文信息引导的融合模块，利用交叉注意力将语义丰富的低分辨率特征与空间细节丰富的高分辨率特征相融合。在涵盖MRI、CT、超声、内窥镜和皮肤镜五种成像模态的六个公共基准数据集上的大量实验表明，在有限样本条件下，DINO-AugSeg始终优于现有方法。结果凸显了结合小波域增强与上下文融合对于构建鲁棒特征表示的有效性，表明DINO-AugSeg是推进少样本医学图像分割的一个有前景的方向。代码与数据将在https://github.com/apple1986/DINO-AugSeg 公开。