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

2017

AUTHORS

Jinzheng Cai , Le Lu , Yuanpu Xie , Fuyong Xing , Lin Yang

ABSTRACT

Deep neural networks have demonstrated very promising performance on accurate segmentation of challenging organs (e.g., pancreas) in abdominal CT and MRI scans. The current deep learning approaches conduct pancreas segmentation by processing sequences of 2D image slices independently through deep, dense per-pixel masking for each image, without explicitly enforcing spatial consistency constraint on segmentation of successive slices. We propose a new convolutional/recurrent neural network architecture to address the contextual learning and segmentation consistency problem. A deep convolutional sub-network is first designed and pre-trained from scratch. The output layer of this network module is then connected to recurrent layers and can be fine-tuned for contextual learning, in an end-to-end manner. Our recurrent sub-network is a type of Long short-term memory (LSTM) network that performs segmentation on an image by integrating its neighboring slice segmentation predictions, in the form of a dependent sequence processing. Additionally, a novel segmentation-direct loss function (named Jaccard Loss) is proposed and deep networks are trained to optimize Jaccard Index (JI) directly. Extensive experiments are conducted to validate our proposed deep models, on quantitative pancreas segmentation using both CT and MRI scans. Our method outperforms the state-of-the-art work on CT [11] and MRI pancreas segmentation [1], respectively. More... »

PAGES

674-682

References to SciGraph publications

  • 2016. Regression Forest-Based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2016
  • 2016. Dense Volume-to-Volume Vascular Boundary Detection in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION -- MICCAI 2016
  • 2015. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION -- MICCAI 2015
  • 2016. Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2016
  • 2016. Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2016
  • 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

    978-3-319-66178-0
    978-3-319-66179-7

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-66179-7_77

    DOI

    http://dx.doi.org/10.1007/978-3-319-66179-7_77

    DIMENSIONS

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