ScribbleVS：通过动态竞争伪标签选择实现涂鸦监督的医学图像分割
ScribbleVS: Scribble-Supervised Medical Image Segmentation via Dynamic Competitive Pseudo Label Selection
AI总结：
针对医学图像分割像素级标注成本高、涂鸦监督训练难且伪标签方法易受噪声干扰的问题，提出ScribbleVS框架，通过区域伪标签扩散和动态竞争选择模块优化伪标签，在多个数据集上实现接近全监督模型的分割精度。
摘要：
在临床医学中，精准的图像分割可为临床医生提供有力支持。然而，获取高质量分割结果通常需要大量像素级标注，这类标注耗时费力且成本高昂。涂鸦标注通过提升标注效率，提供了一种更具成本效益的替代方案。尽管如此，利用这种稀疏监督训练可靠的医学图像分割模型仍是一项重大挑战。部分研究采用伪标签方法增强监督，但这些方法易受噪声干扰。为应对这些挑战，我们提出ScribbleVS，这是一个专为利用涂鸦标注进行学习而设计的框架。我们引入了区域伪标签扩散模块，以扩大监督范围并降低伪标签中存在的噪声影响。此外，我们还引入了动态竞争选择模块，以进一步优化伪标签的选择过程。在ACDC、MSCMRseg、WORD和BraTS2020数据集上开展的实验展现了可观的结果，其分割精度可与全监督模型相媲美。本研究的代码可在https://github.com/ortonwang/ScribbleVS获取。
英文摘要：
In clinical medicine, precise image segmentation can provide substantial support to clinicians. However, obtaining high-quality segmentation typically demands extensive pixel-level annotations, which are labor-intensive and expensive. Scribble annotations offer a more cost-effective alternative by improving labeling efficiency. Nonetheless, using such sparse supervision for training reliable medical image segmentation models remains a significant challenge. Some studies employ pseudo-labeling to enhance supervision, but these methods are susceptible to noise interference. To address these challenges, we introduce ScribbleVS, a framework designed to learn from 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 introduce a Dynamic Competitive Selection module for enhanced refinement in selecting pseudo labels. Experiments conducted on the ACDC, MSCMRseg, WORD, and BraTS2020 datasets demonstrate promising results, achieving segmentation precision comparable to fully supervised models. The codes of this study are available at https://github.com/ortonwang/ScribbleVS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
