用草图标注实现精准医学图像分割，降低人工标注成本。
ScribbleVS: Scribble-Supervised Medical Image Segmentation via Dynamic Competitive Pseudo Label Selection
- 通过动态竞争选择机制优化伪标签质量。
- 在四个医学数据集上达到接近全监督模型的精度。
- 适合需要高效标注的医疗影像研究者使用。
在临床医学中，精确的图像分割可为医生提供重要支持。然而，高质量分割通常需要大量像素级标注，耗时且昂贵。草图标注能显著提升标注效率，降低成本。但利用这种稀疏标注训练可靠医学图像分割模型仍具挑战。现有方法采用伪标签增强监督，却易受噪声干扰。为此，我们提出ScribbleVS框架，从草图标注中学习。引入区域伪标签扩散模块以扩大监督范围，减少伪标签噪声影响；同时设计动态竞争选择模块，进一步优化伪标签选取。在ACDC、MSCMRseg、WORD和BraTS2020数据集上的实验表明，该方法性能接近全监督模型。代码已开源：https://github.com/ortonwang/ScribbleVS。
原文摘要 · Abstract (English)
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.
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