TABNet：一种用于医学图像分割的具有边界感知伪标签的三元组增强自恢复框架
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation
AI总结：
本文提出TAB Net弱监督医学图像分割框架，通过三元组增强自恢复模块和边界感知伪标签监督模块，在涂鸦标注下实现与全监督方法相当的分割性能。
摘要：
背景与目的：医学图像分割是多种临床应用中的核心任务。然而，获取大规模、完全标注的医学图像数据集既耗时又昂贵。涂鸦标注作为一种稀疏标注形式，为医学图像分割提供了一种高效且成本较低的替代方案。然而，涂鸦标注的稀疏性限制了目标区域的特征学习，并且缺乏足够的边界监督，这给分割网络的训练带来了显著挑战。方法：我们提出TAB Net，一种新颖的弱监督医学图像分割框架，由两个关键组件组成：三元组增强自恢复（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增强特征学习：强度变换提高模型对纹理和对比度变化的敏感性，cutout通过遮蔽关键区域迫使网络捕捉局部解剖结构，jigsaw增强通过破坏空间连续性来加强对全局解剖布局的建模。通过引导网络从多样化的增强输入中恢复完整掩码，TAS在稀疏监督下促进了对医学图像更深层的语义理解。BAP模块通过将双分支预测融合为损失加权伪标签，并引入边界感知损失进行细粒度轮廓细化，从而增强伪监督精度和边界建模。结果：在两个公开数据集ACDC和MSCMR seg上的实验评估表明，TAB Net在基于涂鸦的弱监督分割方面显著优于最先进的方法。此外，它实现了与全监督方法相当的性能。
英文摘要：
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.
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