arXiv 2502.073312025-02cs.CV

ERANet：边缘替换增强用于半监督半月板分割的原型一致性对齐与条件自训练

ERANet: Edge Replacement Augmentation for Semi-Supervised Meniscus Segmentation with Prototype Consistency Alignment and Conditional Self-Training

The Chinese University of Hong Kong（香港中文大学）；University of Edinburgh（爱丁堡大学）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；The Chinese University of Hong Kong Medical Centre（香港中文大学医学中心）；Southeast University（东南大学）；Xi'an Jiaotong Liverpool University（西交利物浦大学）；Nanchang University（南昌大学）

Siyue Li, Yongcheng Yao, Junru Zhong, Shutian Zhao, Fan Xiao, Tim-Yun Michael Ong, Ki-Wai Kevin Ho, James F. Griffith, Yudong Zhang, Shuihua Wang, Jin Hong, Weitian Chen

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AI总结：

ERANet通过边缘替换增强、原型一致性对齐和条件自训练策略，提升半监督半月板分割的性能和鲁棒性。

摘要：

手动分割工作量大，自动分割仍面临挑战，因为半月板形态的固有变异性、部分体积效应以及半月板与周围组织之间的低对比度。为了解决这些挑战，我们提出了ERANet，一种创新的半监督框架，用于半月板分割，通过先进的增强和学习策略有效利用标记和未标记图像。ERANet集成了三个关键组件：边缘替换增强（ERA）、原型一致性对齐（PCA）以及在均值教师架构中的条件自训练（CST）策略。ERA通过模拟半月板变化引入解剖学相关的扰动，确保增强与结构上下文对齐。PCA通过对齐类内特征并促进紧凑、判别性的特征表示来提高分割性能，特别是在标注数据有限的情况下。CST通过迭代优化伪标签并减轻训练中的标签噪声影响来提高分割鲁棒性。这些创新使ERANet成为一种稳健且可扩展的半月板分割解决方案，有效解决了实际应用中的关键障碍。我们全面验证了ERANet在3D双回波稳态（DESS）和3D快速/涡流回波（FSE/TSE）MRI序列上的性能。结果表明，ERANet的性能优于最先进的方法。所提出的框架即使在训练数据有限的情况下也能实现可靠且准确的半月板结构分割。广泛的消融研究进一步突显了ERA、PCA和CST的协同贡献，巩固了ERANet作为医学影像中半监督半月板分割的变革性解决方案。

英文摘要：

Manual segmentation is labor-intensive, and automatic segmentation remains challenging due to the inherent variability in meniscal morphology, partial volume effects, and low contrast between the meniscus and surrounding tissues. To address these challenges, we propose ERANet, an innovative semi-supervised framework for meniscus segmentation that effectively leverages both labeled and unlabeled images through advanced augmentation and learning strategies. ERANet integrates three key components: edge replacement augmentation (ERA), prototype consistency alignment (PCA), and a conditional self-training (CST) strategy within a mean teacher architecture. ERA introduces anatomically relevant perturbations by simulating meniscal variations, ensuring that augmentations align with the structural context. PCA enhances segmentation performance by aligning intra-class features and promoting compact, discriminative feature representations, particularly in scenarios with limited labeled data. CST improves segmentation robustness by iteratively refining pseudo-labels and mitigating the impact of label noise during training. Together, these innovations establish ERANet as a robust and scalable solution for meniscus segmentation, effectively addressing key barriers to practical implementation. We validated ERANet comprehensively on 3D Double Echo Steady State (DESS) and 3D Fast/Turbo Spin Echo (FSE/TSE) MRI sequences. The results demonstrate the superior performance of ERANet compared to state-of-the-art methods. The proposed framework achieves reliable and accurate segmentation of meniscus structures, even when trained on minimal labeled data. Extensive ablation studies further highlight the synergistic contributions of ERA, PCA, and CST, solidifying ERANet as a transformative solution for semi-supervised meniscus segmentation in medical imaging.

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