Deep learning for corpus callosum segmentation in brain magnetic resonance images

Abstract

In this work we present a novel method to segment Corpus Callosum in Magnetic Resonance Images (MRI) using U-Net, a Fully Convolutional Neural Network. We trained the U-Net using two public datasets and evaluated the trained model in a test set also obtained from these two public datasets. Results are obtained making comparisons using the Structural Similarity Index (SSIM) and Dice Coefficient between the Ground Truth and the Predicted image.