发表机构
Fuzhou University; Qingdao University（福州大学; 青岛大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对少样本涂鸦监督医学图像分割中样本稀缺和监督稀疏问题，提出双层协作学习框架，上层提供区域结构先验，下层生成可靠伪标签，上下层双向交互协作，在相关数据集上优于现有方法。
AI 中文摘要
涂鸦注释为医学图像分割中代价高昂的逐像素标记提供了一种有效替代方案。但在实际临床场景中，带涂鸦注释的样本往往有限，带来稀疏监督和注释样本稀缺的双重挑战。为打破瓶颈，提出双层协作学习框架。上层可学习超像素模型为下层分割提供区域结构先验，同时进行基于超像素的区域伪标签传播和空间先验引导滤波策略生成可靠密集伪标签。下层分割模型在当前超像素引导下学习的解剖语义反馈到上层，进一步推动其学习与分割任务更匹配的区域结构表示。通过上下层双向交互和协作学习，该框架在少样本涂鸦监督设置下在ACDC和前列腺数据集上显著优于现有最先进的涂鸦监督方法。
英文摘要
Scribble annotations offer an efficient alternative to costly pixel-wise labeling for medical image segmentation, yet in real clinical scenarios, scribble-annotated samples are often still limited, imposing the dual challenges of sparse supervision and annotated sample scarcity. These compounded constraints severely deprive models of the structural evidence needed for complete region recovery and precise boundary delineation. To break this bottleneck, we propose a bi-level collaborative learning framework for few-shot scribble-supervised medical image segmentation. Specifically, an upper-level learnable superpixel model is introduced to provide region-structural priors for lower-level segmentation, while superpixel-based region-wise pseudo-label propagation and a spatial-prior-guided filtering strategy are performed to generate reliable dense pseudo-labels for segmentation learning. Meanwhile, the anatomical semantics learned by the lower-level segmentation model under the guidance of the current superpixels are fed back to the upper level, further driving it to learn region-structural representations better aligned with the segmentation task. Through bidirectional interaction and collaborative learning between the upper and lower levels, the proposed framework significantly outperforms existing state-of-the-art scribble-supervised methods on the ACDC and Prostate datasets under the few-shot scribble-supervised setting.
