利用影像空间配准信息提升少样本医学图像分割效果
Learning Semi-Supervised Medical Image Segmentation from Spatial Registration
- 通过配准变换构建对比学习的正样本，增强语义匹配
- 仅需1个标注病例即实现优异分割性能，跨数据集验证有效
- 适合标注数据稀缺的医学影像研究者使用
半监督医学图像分割在仅有少量标注数据和大量未标注数据的情况下展现出潜力。然而，现有方法忽略了图像体积间空间配准变换中蕴含的潜在无监督语义信息。为此，我们提出CCT-R框架，结合注册信息进行对比跨教学学习。该框架引入两个模块：注册监督损失（RSL）和注册增强正样本采样（REPS）。RSL利用已标注与未标注体数据对间的变换所导出的分割知识，生成额外的伪标签；REPS通过注册变换识别跨体积间的解剖对应正样本，增强对比学习效果。在两个具有挑战性的医学分割基准测试上，CCT-R在多种半监督设置下均表现出显著优势，最低仅需1个标注病例。代码已开源。
原文摘要 · Abstract (English)
Semi-supervised medical image segmentation has shown promise in training models with limited labeled data and abundant unlabeled data. However, state-of-the-art methods ignore a potentially valuable source of unsupervised semantic information -- spatial registration transforms between image volumes. To address this, we propose CCT-R, a contrastive cross-teaching framework incorporating registration information. To leverage the semantic information available in registrations between volume pairs, CCT-R incorporates two proposed modules: Registration Supervision Loss (RSL) and Registration-Enhanced Positive Sampling (REPS). The RSL leverages segmentation knowledge derived from transforms between labeled and unlabeled volume pairs, providing an additional source of pseudo-labels. REPS enhances contrastive learning by identifying anatomically-corresponding positives across volumes using registration transforms. Experimental results on two challenging medical segmentation benchmarks demonstrate the effectiveness and superiority of CCT-R across various semi-supervised settings, with as few as one labeled case. Our code is available at https://github.com/kathyliu579/ContrastiveCross-teachingWithRegistration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。