Unsupervised Segmentation Framework with Active Contour Models for Cine Cardiac MRI

Deep learning methods have made remarkable progress in medical image segmentation tasks, but these methods require enough labeled data, which tends to be difficult for medical tasks. To tack this issue, we propose an unsupervised segmentation framework by combining deep learning networks with the active contour model. We design an iterative loop process that the network can be trained with outputs from the active contour model and the active contour model can be initialized by coarse predictions from the network. In this way, our approach can train the segmentation networks iterative with no annotations but only one initialization for the active contour model at the beginning. We evaluate our approach in the task of cine cardiac MRI segmentation and get very competitive results.