Measuring the distribution of major brain tissues using magnetic resonance (MR) images has attracted extensive research efforts. Due to its remarkable success, deep learning-based image segmentation has been applied to this problem, in which the size of patches usually represents a tradeoff between complexity and accuracy. In this paper, we propose the multi-size-and-position neural network (MSPNN) for brain MR image segmentation. Our contributions include (1) jointly using U-Nets trained on large patches and back propagation neural networks (BPNNs) trained on small patches for segmentation, and (2) adopting the convolutional auto-encoder (CAE) to restore MR images before applying them to BPNNs. We have evaluated this algorithm against five widely used brain MR image segmentation approaches on both synthetic and real MR studies. Our results indicate that the proposed algorithm can segment brain MR images effectively and provide precise distribution of major brain tissues.


Brain MR image segmentation Deep learning U-Net Convolutional Auto-Encoder (CAE) Back Propagation Neural Network (BPNN) 



This work was supported by the National Natural Science Foundation of China under Grants 61471297.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Shaanxi Key Lab of Speech and Image Information Processing (SAIIP), School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China
  2. 2.School of Computer Science and Technology, Centre for Multidisciplinary Convergence Computing (CMCC)Northwestern Polytechnical UniversityXi’anPeople’s Republic of China

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