Abstract
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.
Abstract (translated)
高质量的像素级医学图像标注对于监督分割任务至关重要，但获取这样的标注既耗时又需要医疗专业知识。为了解决这一挑战，我们提出了一种新颖的粗到细分割框架，该框架完全依赖于粗略级别的标注（包括目标和补充绘制），即使这些标注中存在固有的噪声。此框架通过引入转换矩阵来建模粗略注释中的不准确和不完整区域。通过在多个粗略注释集上联合训练，它逐步细化网络输出，并推断出真正的分割分布，从而通过基于矩阵的模型实现对精确标签的稳健近似。 为了验证所提出方法的灵活性和有效性，我们在两个公开的心脏成像数据集ACDC和MSCMRseg上展示了结果，并进一步在UK Biobank数据集中评估了其性能。实验结果显示，我们的方法超过了现有的弱监督方法，在某些指标上接近于完全监督的方法。
URL
https://arxiv.org/abs/2508.02844
