Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation
Journal:
arXiv
Published Date:
May 30, 2025
Abstract
Both limited annotation and domain shift are prevalent challenges in medical
image segmentation. Traditional semi-supervised segmentation and unsupervised
domain adaptation methods address one of these issues separately. However, the
coexistence of limited annotation and domain shift is quite common, which
motivates us to introduce a novel and challenging scenario: Mixed Domain
Semi-supervised medical image Segmentation (MiDSS), where limited labeled data
from a single domain and a large amount of unlabeled data from multiple
domains. To tackle this issue, we propose the UST-RUN framework, which fully
leverages intermediate domain information to facilitate knowledge transfer. We
employ Unified Copy-paste (UCP) to construct intermediate domains, and propose
a Symmetric GuiDance training strategy (SymGD) to supervise unlabeled data by
merging pseudo-labels from intermediate samples. Subsequently, we introduce a
Training Process aware Random Amplitude MixUp (TP-RAM) to progressively
incorporate style-transition components into intermediate samples. To generate
more diverse intermediate samples, we further select reliable samples with
high-quality pseudo-labels, which are then mixed with other unlabeled data.
Additionally, we generate sophisticated intermediate samples with high-quality
pseudo-labels for unreliable samples, ensuring effective knowledge transfer for
them. Extensive experiments on four public datasets demonstrate the superiority
of UST-RUN. Notably, UST-RUN achieves a 12.94% improvement in Dice score on the
Prostate dataset. Our code is available at https://github.com/MQinghe/UST-RUN
