Self-training with dual uncertainty for semi-supervised MRI image segmentation

Zhanhong Qiu, Haitao Gan, Ming Shi, Zhongwei Huang, Zhi Yang

Abstract: Highlights•An uncertainty-based self-training framework for MRI image segmentation.•Sample-level uncertainty is used to prioritize unlabeled data segmentation.•Pixel-level uncertainty is used to obtain high-quality pseudo labels.•Proposed method can effectively utilize unlabeled data to train high-accuracy models.