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3D patchwise U-net with transition layers for MR brain segmentation

Title
3D patchwise U-net with transition layers for MR brain segmentation
Authors
Acevedo, Miguel Andres LunaPark, Sang Hyun
DGIST Authors
Park, Sang Hyun
Issue Date
2018-09-16
Citation
4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018, 394-403
Type
Conference
ISBN
9783030117221
ISSN
0302-9743
Abstract
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. © Springer Nature Switzerland AG 2019.
URI
http://hdl.handle.net/20.500.11750/9753
DOI
10.1007/978-3-030-11723-8_40
Publisher
Springer Verlag
Related Researcher
  • Author Park, Sang Hyun Medical Image & Signal Processing Lab
  • Research Interests 컴퓨터비전, 인공지능, 의료영상처리
Files:
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Collection:
Department of Robotics EngineeringMedical Image & Signal Processing Lab2. Conference Papers


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