HELPNet: Hierarchical Perturbations Consistency and Entropy-guided Ensemble for Scribble Supervised Medical Image Segmentation
Journal:
arXiv
Published Date:
Dec 25, 2024
Abstract
Creating fully annotated labels for medical image segmentation is
prohibitively time-intensive and costly, emphasizing the necessity for
innovative approaches that minimize reliance on detailed annotations. Scribble
annotations offer a more cost-effective alternative, significantly reducing the
expenses associated with full annotations. However, scribble annotations offer
limited and imprecise information, failing to capture the detailed structural
and boundary characteristics necessary for accurate organ delineation. To
address these challenges, we propose HELPNet, a novel scribble-based weakly
supervised segmentation framework, designed to bridge the gap between
annotation efficiency and segmentation performance. HELPNet integrates three
modules. The Hierarchical perturbations consistency (HPC) module enhances
feature learning by employing density-controlled jigsaw perturbations across
global, local, and focal views, enabling robust modeling of multi-scale
structural representations. Building on this, the Entropy-guided pseudo-label
(EGPL) module evaluates the confidence of segmentation predictions using
entropy, generating high-quality pseudo-labels. Finally, the structural prior
refinement (SPR) module incorporates connectivity and bounded priors to enhance
the precision and reliability and pseudo-labels. Experimental results on three
public datasets ACDC, MSCMRseg, and CHAOS show that HELPNet significantly
outperforms state-of-the-art methods for scribble-based weakly supervised
segmentation and achieves performance comparable to fully supervised methods.
The code is available at https://github.com/IPMI-NWU/HELPNet.
