Approaches for the automatic segmentation of magnetic resonance (MR) images. Machine learning models segment images to identify image features in consecutive frames at different levels of resolution. A neural network block is applied to groups of MR images to produce primary feature maps at two or more levels of resolution. The images in a given group of MR images may correspond to a cycle and have a temporal order. A second RNN block is applied to the primary feature maps to produce two or more output tensors at corresponding levels of resolution. A segmentation block is applied to the two or more output tensors to produce a probability map for the MR images. The first neural network block may be a convolutional neural network (CNN) block. The second neural network block may be a convolutional long short-term (LSTM) block.
Multi-level convolutional LSTM model for the segmentation of MR images