C3S3: Complementary Competition and Contrastive Selection for Semi-Supervised Medical Image Segmentation
Journal:
arXiv
Published Date:
Jun 9, 2025
Abstract
For the immanent challenge of insufficiently annotated samples in the medical
field, semi-supervised medical image segmentation (SSMIS) offers a promising
solution. Despite achieving impressive results in delineating primary target
areas, most current methodologies struggle to precisely capture the subtle
details of boundaries. This deficiency often leads to significant diagnostic
inaccuracies. To tackle this issue, we introduce C3S3, a novel semi-supervised
segmentation model that synergistically integrates complementary competition
and contrastive selection. This design significantly sharpens boundary
delineation and enhances overall precision. Specifically, we develop an
$\textit{Outcome-Driven Contrastive Learning}$ module dedicated to refining
boundary localization. Additionally, we incorporate a $\textit{Dynamic
Complementary Competition}$ module that leverages two high-performing
sub-networks to generate pseudo-labels, thereby further improving segmentation
quality. The proposed C3S3 undergoes rigorous validation on two publicly
accessible datasets, encompassing the practices of both MRI and CT scans. The
results demonstrate that our method achieves superior performance compared to
previous cutting-edge competitors. Especially, on the 95HD and ASD metrics, our
approach achieves a notable improvement of at least $6\%$, highlighting the
significant advancements. The code is available at
https://github.com/Y-TARL/C3S3.
