Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2016

AUTHORS

Jinzheng Cai , Le Lu , Zizhao Zhang , Fuyong Xing , Lin Yang , Qian Yin

ABSTRACT

Automated pancreas segmentation in medical images is a prerequisite for many clinical applications, such as diabetes inspection, pancreatic cancer diagnosis, and surgical planing. In this paper, we formulate pancreas segmentation in magnetic resonance imaging (MRI) scans as a graph based decision fusion process combined with deep convolutional neural networks (CNN). Our approach conducts pancreatic detection and boundary segmentation with two types of CNN models respectively: 1) the tissue detection step to differentiate pancreas and non-pancreas tissue with spatial intensity context; 2) the boundary detection step to allocate the semantic boundaries of pancreas. Both detection results of the two networks are fused together as the initialization of a conditional random field (CRF) framework to obtain the final segmentation output. Our approach achieves the mean dice similarity coefficient (DSC) 76.1% with the standard deviation of 8.7% in a dataset containing 78 abdominal MRI scans. The proposed algorithm achieves the best results compared with other state of the arts. More... »

PAGES

442-450

References to SciGraph publications

  • 2015. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION -- MICCAI 2015
  • 2014. Geodesic Patch-Based Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2014
  • Book

    TITLE

    Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

    ISBN

    978-3-319-46722-1
    978-3-319-46723-8

    From Grant

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-46723-8_51

    DOI

    http://dx.doi.org/10.1007/978-3-319-46723-8_51

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1032895350

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28083570


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