Scribble-based weakly supervised segmentation techniques offer comparable performance to fully supervised methods while significantly reducing annotation costs, making them an appealing alternative. Existing methods often rely on auxiliary tasks to enforce semantic consistency and use hard pseudo labels for supervision. However, these methods often overlook the unique requirements of models trained with sparse annotations. Since the model must predict pixel-wise segmentation maps with limited annotations, the ability to handle varying levels of annotation richness is critical. In this paper, we adopt the principle of `from few to more' and propose MaCo, a weakly supervised framework designed for medical image segmentation. MaCo employs masked context modeling (MCM) and continuous pseudo labels (CPL). MCM uses an attention-based masking strategy to disrupt the input image, compelling the model's predictions to remain consistent with those of the original image. CPL converts scribble annotations into continuous pixel-wise labels by applying an exponential decay function to distance maps, resulting in continuous maps that represent the confidence of each pixel belonging to a specific category, rather than using hard pseudo labels. We evaluate MaCo against other weakly supervised methods using three public datasets. The results indicate that MaCo outperforms competing methods across all datasets, setting a new record in weakly supervised medical image segmentation.

本研究解决了当前涂鸦基础弱监督分割技术在稀疏标注下的性能和一致性问题。提出的MaCo框架结合了掩模上下文建模和连续伪标签，通过提升模型对不均匀标注的处理能力，改进了医学图像分割的效果。实验表明，MaCo在多个公共数据集上表现优越，推动了弱监督医学图像分割的发展。

从少到多：基于涂鸦的医学图像分割通过掩模上下文建模和连续伪标签