In clinical medicine, precise image segmentation can provide substantial support to clinicians. However, achieving such precision often requires a large amount of finely annotated data, which can be costly. Scribble annotation presents a more efficient alternative, boosting labeling efficiency. However, utilizing such minimal supervision for medical image segmentation training, especially with scribble annotations, poses significant challenges. To address these challenges, we introduce ScribbleVS, a novel framework that leverages scribble annotations. We introduce a Regional Pseudo Labels Diffusion Module to expand the scope of supervision and reduce the impact of noise present in pseudo labels. Additionally, we propose a Dynamic Competitive Selection module for enhanced refinement in selecting pseudo labels. Experiments conducted on the ACDC and MSCMRseg datasets have demonstrated promising results, achieving performance levels that even exceed those of fully supervised methodologies. The codes of this study are available at https://github.com/ortonwang/ScribbleVS.

翻译：在临床医学中，精确的图像分割能为临床医生提供重要支持。然而，实现这种精度通常需要大量精细标注的数据，成本高昂。涂鸦标注提供了一种更高效的替代方案，可显著提升标注效率。然而，利用这种极弱监督进行医学图像分割训练，尤其是基于涂鸦标注，仍面临重大挑战。为应对这些挑战，我们提出了ScribbleVS，一种利用涂鸦标注的新型框架。我们引入了区域伪标签扩散模块，以扩展监督范围并降低伪标签中噪声的影响。此外，我们提出了动态竞争选择模块，用于在伪标签选择过程中实现增强的精细化处理。在ACDC和MSCMRseg数据集上进行的实验取得了显著成果，其性能甚至超越了全监督方法。本研究的代码公开于https://github.com/ortonwang/ScribbleVS。