TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation
Journal:
arXiv
Published Date:
Jul 3, 2025
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.
