用粗糙标注训练医学图像分割，通过矩阵建模逐步逼近精确结果。
RefineSeg: Dual Coarse-to-Fine Learning for Medical Image Segmentation
- 引入转移矩阵建模粗标注中的错漏区域
- 在多个粗标注集上联合训练，实现渐进式精化
- 在心脏影像数据集上接近全监督效果，适合标注成本高的场景
高质量像素级医学图像标注对监督分割任务至关重要，但获取此类标注成本高且需专业医疗知识。为解决此问题，我们提出一种完全依赖粗略标注（含目标与辅助草图）的端到端粗到细分割框架，尽管这些标注存在噪声。该框架通过引入转移矩阵来建模粗标注中的不准确和不完整区域。在多个粗标注集上联合训练，使网络输出逐步精化，并通过基于矩阵的建模推断真实分割分布，从而稳健逼近精确标签。为验证方法的灵活性与有效性，我们在两个公开心脏影像数据集ACDC和MSCMRseg上进行实验，并进一步在UK Biobank数据集上评估性能。实验结果表明，该方法优于现有弱监督分割方法，且接近全监督方法的表现。
原文摘要 · Abstract (English)
High-quality pixel-level annotations of medical images are essential for supervised segmentation tasks, but obtaining such annotations is costly and requires medical expertise. To address this challenge, we propose a novel coarse-to-fine segmentation framework that relies entirely on coarse-level annotations, encompassing both target and complementary drawings, despite their inherent noise. The framework works by introducing transition matrices in order to model the inaccurate and incomplete regions in the coarse annotations. By jointly training on multiple sets of coarse annotations, it progressively refines the network's outputs and infers the true segmentation distribution, achieving a robust approximation of precise labels through matrix-based modeling. To validate the flexibility and effectiveness of the proposed method, we demonstrate the results on two public cardiac imaging datasets, ACDC and MSCMRseg, and further evaluate its performance on the UK Biobank dataset. Experimental results indicate that our approach surpasses the state-of-the-art weakly supervised methods and closely matches the fully supervised approach.
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