Abstract
Background and objective: Medical image segmentation is a core task in various clinical applications. However, acquiring large-scale, fully annotated medical image datasets is both time-consuming and costly. Scribble annotations, as a form of sparse labeling, provide an efficient and cost-effective alternative for medical image segmentation. However, the sparsity of scribble annotations limits the feature learning of the target region and lacks sufficient boundary supervision, which poses significant challenges for training segmentation networks. Methods: We propose TAB Net, a novel weakly-supervised medical image segmentation framework, consisting of two key components: the triplet augmentation self-recovery (TAS) module and the boundary-aware pseudo-label supervision (BAP) module. The TAS module enhances feature learning through three complementary augmentation strategies: intensity transformation improves the model's sensitivity to texture and contrast variations, cutout forces the network to capture local anatomical structures by masking key regions, and jigsaw augmentation strengthens the modeling of global anatomical layout by disrupting spatial continuity. By guiding the network to recover complete masks from diverse augmented inputs, TAS promotes a deeper semantic understanding of medical images under sparse supervision. The BAP module enhances pseudo-supervision accuracy and boundary modeling by fusing dual-branch predictions into a loss-weighted pseudo-label and introducing a boundary-aware loss for fine-grained contour refinement. Results: Experimental evaluations on two public datasets, ACDC and MSCMR seg, demonstrate that TAB Net significantly outperforms state-of-the-art methods for scribble-based weakly supervised segmentation. Moreover, it achieves performance comparable to that of fully supervised methods.
Abstract (translated)
背景和目标：医学图像分割是临床应用中的核心任务之一。然而，获取大规模、全面标注的医学影像数据集既耗时又昂贵。草图注解作为一种稀疏标签形式，为医学图像分割提供了一种高效且成本效益高的替代方案。但是，草图注解的稀疏性限制了目标区域的特征学习，并缺乏足够的边界监督，这给训练分割网络带来了重大挑战。 方法：我们提出了一种新型弱监督医学影像分割框架TAB Net，该框架包含两个关键组成部分：三元组增强自我恢复（TAS）模块和边界感知伪标签监督（BAP）模块。TAS模块通过三种互补的增强策略来提高特征学习能力：强度变换增强了模型对纹理和对比度变化的敏感性；裁剪操作迫使网络在遮盖关键区域的情况下捕捉局部解剖结构；拼图增广则通过破坏空间连续性来加强全局解剖布局的建模。TAS模块引导网络从多样化的增强输入中恢复完整的掩码，从而在稀疏监督下促进医学影像的语义理解。 BAP模块通过将双支路预测融合成一个加权伪标签，并引入边界感知损失进行细粒度轮廓精炼来提高伪监督准确性及边界建模。这种方法不仅提高了伪标签的质量，还增强了对图像边缘细节的关注和建模能力。 结果：在两个公开数据集ACDC和MSCMR seg上的实验评估表明，TAB Net显著优于基于草图的弱监督分割方法中的最新技术，并且其性能与全监督方法相当。
URL
https://arxiv.org/abs/2507.02399
