3D Patchwise U-Net with Transition Layers for MR Brain Segmentation

  • Miguel Luna
  • Sang Hyun ParkEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)


We propose a new patch based 3D convolutional neural network to automatically segment multiple brain structures on Magnetic Resonance (MR) images. The proposed network consists of encoding layers to extract informative features and decoding layers to reconstruct the segmentation labels. Unlike the conventional U-net model, we use transition layers between the encoding layers and the decoding layers to emphasize the impact of feature maps in the decoding layers. Moreover, we use batch normalization on every convolution layer to make a well generalized model. Finally, we utilize a new loss function which can normalize the categorical cross entropy to accurately segment the relatively small interest regions which are opt to be misclassified. The proposed method ranked 1\(^{st}\) over 22 participants at the MRBrainS18 segmentation challenge at MICCAI 2018.


Convolutional neural network Brain MR image Semantic segmentation Transition layer Normalized cross entropy 



This research was supported by Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (2018R1D1A1B07044473).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Robotics EngineeringDGISTDaeguSouth Korea

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