Efficient Symmetry-driven Fully Convolutional Network for Multimodal Brain Tumor Segmentation

Haocheng Shen, Jianguo Zhang, Weishi Zheng

Research output: Contribution to conferencePaper

7 Citations (Scopus)
119 Downloads (Pure)

Abstract

In this paper, we present a novel and efficient method for brain tumor (and sub regions) segmentation in multimodal MR images based on a fully convolutional network (FCN) that enables end-to-end training and fast inference. Our structure consists of a downsampling path and three upsampling paths, which extract multi-level contextual information by concatenating hierarchical feature representation from each upsampling path. Meanwhile, we introduce a symmetry-driven FCN by the proposal of using symmetry difference images. The model was evaluated on Brain Tumor Image Segmentation Benchmark (BRATS) 2013 challenge dataset and achieved the state-of-the-art results while the computational cost is less than competitors.
Original languageEnglish
Pages3864-3868
Number of pages5
DOIs
Publication statusPublished - 17 Sep 2017
EventThe International Conference on Image Processing 2017 - China National Convention Center, Beijing, China
Duration: 17 Sep 201720 Dec 2017

Conference

ConferenceThe International Conference on Image Processing 2017
Abbreviated titleIEEE ICIP 2017
CountryChina
CityBeijing
Period17/09/1720/12/17

Keywords

  • FCN
  • brain tumor segmentati

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  • Cite this

    Shen, H., Zhang, J., & Zheng, W. (2017). Efficient Symmetry-driven Fully Convolutional Network for Multimodal Brain Tumor Segmentation. 3864-3868. Paper presented at The International Conference on Image Processing 2017, Beijing, China. https://doi.org/10.1109/ICIP.2017.8297006