Uncertainty-Aware Label Rectification for Domain Adaptive Mitochondria Segmentation

Mitochondria segmentation from electron microscopy images has seen great progress, especially for learning-based methods. However, since the learning of model requires massive annotations, it is time and labour expensive to learn a specific model for each acquired dataset. On the other hand, it is challenging to generalize a learned model to datasets of unknown species or those acquired by unknown devices, mainly due to the difference of data distributions. In this paper, we study unsupervised domain adaptation to enhance the generalization capacity, where no annotation for target datasets is required. We start from an effective solution, which learns the target data distribution with pseudo labels predicted by a source-domain model. However, the obtained pseudo labels are usually noisy due to the domain gap. To address this issue, we propose an uncertainty-aware model to rectify noisy labels. Specifically, we insert Monte-Carlo dropout layers to a UNet backbone, where the uncertainty is measured by the standard deviation of predictions. Experiments on MitoEM and FAFB datasets demonstrate the superior performance of proposed model, in terms of the adaptations between different species and acquisition devices.