A FUSION ALGORITHM: FULLY CONVOLUTIONAL NETWORKS AND STUDENT'S-T MIXTURE MODEL FOR BRAIN MAGNETIC RESONANCE IMAGING SEGMENTATION

Deep convolutional neural networks (DCNN) are applied widely in image recognition and segmentation. In this paper, a novel algorithm (U-SMM) which incorporates the convolutional neural network U-net and modified Student's-t mixture model (MSMM) is provided. The proposed framework considers the spatial relationships in segmenting medical images with MSMM and then uses U-net to correct the mistake labels made by unsupervised method. Because a few error-segmented regions may be caused by MSMM, the U-net is then applied to learn the features of these regions. In our method, the purpose of U-net is to assist the MSMM in improving the accuracy of segmentation and acquiring rich details in image segmentation tasks. Finally, the proposed framework is evaluated on real MR images with several related supervised and unsupervised methods, and the experimental results confirm the effectiveness of our approach.