Alleviate and Mining: Rethinking Unsupervised Domain Adaptation for Mitochondria Segmentation from Pseudo-Label Perspective

Abstract

Mitochondria segmentation from electron microscopy (EM) images plays a crucial role in biological and medical research. However, models trained on source domains often suffer from performance degradation when applied to target domains due to domain shift. Unsupervised domain adaptation (UDA) methods have been proposed to address this issue, but they often overlook the reliability of pseudo-labels and the effectiveness of supervision signals. In this paper, we propose R4MITO, a novel UDA framework for robust mitochondria segmentation. First, we introduce Reliable Prototype Pseudo-labels to mitigate the inconsistency of class-level features between across domains by leveraging source prototypes to model target prototypes. Second, we devise Correlation-wise Consistency Regularization to exploit inter-pixel correlations, aligning agent-level correlations under various perturbations. Third, we propose Rank-aware Relationship Consistency Regularization to fully utilize the rich information encoded in inter-agent relationships by imposing rank-aware constraints on agent-ranking probability distributions. Extensive experiments on multiple EM datasets demonstrate the superiority of our R4MITO over existing state-of-the-art UDA methods for mitochondria segmentation.