Segmentation of Brain Structures MR Images via Semi-Supervised Learning

The precise segmentation of brain structures is of great importance in quantitatively analyzing brain medical resonance images. In recent years, more and more experts attempt to apply semi-supervised learning to medical image segmentation tasks, since it could make use of the rich unlabeled data. Based on this, we modified a segmentation model based on semi-supervised learning for automatically segmenting brain structures. We tested model on two hippocampus public datasets and results show that our model has considerable segmentation performance compared with that of model trained in a supervised manner, which illustrates the effectiveness and potential of our model.