Active Semi-supervised Learning based on Global Uncertainty Variation with Noise Resistance

Automatic and accurate fetal brain segmentation is essential for congenital disease diagnosis and treatment. However, voxel-wise manual annotation is laborious, and the annotation quality strongly depends on the annotator's professional knowledge and clinical experience. This problem contradicts the data-hungry nature of deep learning, especially for medical image segmentation. To reduce the consumption of annotation, in this paper, we propose a novel active semi-supervised algorithm for fetal brain tissue segmentation that incorporates the active learning techniques into semi-supervised methods to minimize labeling costs. Specifically, we present a new active learning selection strategy that leverages the global uncertainty variation of a sample to measure its informativeness and adaptively adjust the time to perform active learning according to the learning state of the network. Furthermore, we design a non-parameter pool attention (PA) module to refine the prediction of the model and resist noise effectively. In addition, we introduce symmetric soft cross entropy (SSCE) loss as an unsupervised loss function to resist noise further. Extensive experiments on two fetal brain tissue segmentation datasets demonstrate the effectiveness of our model, outperforming state-of-the-art approaches. Associated codes can be accessed at: https://github.com/Dreamer1209/ASL.