Boundary-Aware Fully Convolutional Network for Brain Tumor Segmentation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017

AUTHORS

Haocheng Shen , Ruixuan Wang , Jianguo Zhang , Stephen J. McKenna

ABSTRACT

We propose a novel, multi-task, fully convolutional network (FCN) architecture for automatic segmentation of brain tumor. This network extracts multi-level contextual information by concatenating hierarchical feature representations extracted from multimodal MR images along with their symmetric-difference images. It achieves improved segmentation performance by incorporating boundary information directly into the loss function. The proposed method was evaluated on the BRATS13 and BRATS15 datasets and compared with competing methods on the BRATS13 testing set. Segmented tumor boundaries obtained were better than those obtained by single-task FCN and by FCN with CRF. The method is among the most accurate available and has relatively low computational cost at test time. More... »

PAGES

433-441

References to SciGraph publications

  • 2014. Combining Generative Models for Multifocal Glioma Segmentation and Registration in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2014
  • 2016. Gland Instance Segmentation by Deep Multichannel Side Supervision in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2016
  • 2015-04. Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR in NEUROINFORMATICS
  • 2016. Brain Tumor Segmentation Using a Fully Convolutional Neural Network with Conditional Random Fields in BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES
  • 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2015
  • Book

    TITLE

    Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017

    ISBN

    978-3-319-66184-1
    978-3-319-66185-8

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-66185-8_49

    DOI

    http://dx.doi.org/10.1007/978-3-319-66185-8_49

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1091428121


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