用小波变换增强医学图像分割，通过双向复制粘贴提升未标注数据利用效率。
WT-BCP: Wavelet Transform based Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation
- 基于小波变换提取图像高低频特征，实现跨标签与未标签数据的双向内容复制。
- 在2D/3D数据集上达到新高，3D BraTS上达89.1%骰子系数，优于主流方法。
- 适合医疗影像领域研究者，尤其关注半监督学习与低标注成本场景。
半监督医学图像分割（SSMIS）有望减少对稀缺标注数据的依赖。然而，该领域面临标签与未标签数据分布不一致、人工扰动引入训练偏差、原始图像信息（尤其是低频与高频成分）利用不足等挑战。为此，我们提出基于小波变换的双向复制粘贴框架WT-BCP，改进了均值教师模型。通过在标签与未标签图像间随机裁剪区域进行双向复制，并利用小波变换提取低频（LF）与高频（HF）细节，增强未标注数据理解。设计多输入多输出模型XNet-Plus融合小波处理后的特征。一致性训练中，将小波混合图像输入双模型，学生模型输出由伪标签与真实标签联合监督，有效缓解人工扰动带来的偏差。在2D和3D数据集上的大量实验验证了模型有效性。
原文摘要 · Abstract (English)
Semi-supervised medical image segmentation (SSMIS) shows promise in reducing reliance on scarce labeled medical data. However, SSMIS field confronts challenges such as distribution mismatches between labeled and unlabeled data, artificial perturbations causing training biases, and inadequate use of raw image information, especially low-frequency (LF) and high-frequency (HF) components.To address these challenges, we propose a Wavelet Transform based Bidirectional Copy-Paste SSMIS framework, named WT-BCP, which improves upon the Mean Teacher approach. Our method enhances unlabeled data understanding by copying random crops between labeled and unlabeled images and employs WT to extract LF and HF details.We propose a multi-input and multi-output model named XNet-Plus, to receive the fused information after WT. Moreover, consistency training among multiple outputs helps to mitigate learning biases introduced by artificial perturbations. During consistency training, the mixed images resulting from WT are fed into both models, with the student model's output being supervised by pseudo-labels and ground-truth. Extensive experiments conducted on 2D and 3D datasets confirm the effectiveness of our model.Code: https://github.com/simzhangbest/WT-BCP.
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