通过划分同类像素提升心肌图像分割边界精度
Intra-Class Subdivision for Pixel Contrastive Learning: Application to Semi-supervised Cardiac Image Segmentation
- 将同一类像素分为内部与边界两类，区分不同区域表征
- 引入边界对比损失，显著提升边界处特征区分度
- 适合需要高精度边界的医学图像分割任务
我们提出一种针对心脏图像分割的类内子划分像素对比学习（SPCL）框架，以解决边界区域表示污染问题。创新性地提出‘无关样本’概念，用于区分同一类别内部分布于内部和边界区域的像素表征，从而更清晰刻画类内差异。设计了一种新型边界对比损失，专门增强边界区域的表征区分能力。理论分析验证了无关样本与边界对比损失的优势。在公开的心脏影像数据集上的实验表明，SPCL显著提升了分割性能，在分割质量与边界精度方面优于现有方法。代码已开源：https://github.com/Jrstud203/SPCL。
原文摘要 · Abstract (English)
We propose an intra-class subdivision pixel contrastive learning (SPCL) framework for cardiac image segmentation to address representation contamination at boundaries. The novel concept ``Unconcerned sample'' is proposed to distinguish pixel representations at the inner and boundary regions within the same class, facilitating a clearer characterization of intra-class variations. A novel boundary contrastive loss for boundary representations is proposed to enhance representation discrimination across boundaries. The advantages of the unconcerned sample and boundary contrastive loss are analyzed theoretically. Experimental results in public cardiac datasets demonstrate that SPCL significantly improves segmentation performance, outperforming existing methods with respect to segmentation quality and boundary precision. Our code is available at https://github.com/Jrstud203/SPCL.
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