Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2016

AUTHORS

Holger R. Roth , Le Lu , Amal Farag , Andrew Sohn , Ronald M. Summers

ABSTRACT

Accurate automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhibits traditional segmentation methods from achieving high accuracies, especially compared to other organs such as the liver, heart or kidneys. In this paper, we present a holistic learning approach that integrates semantic mid-level cues of deeply-learned organ interior and boundary maps via robust spatial aggregation using random forest. Our method generates boundary preserving pixel-wise class labels for pancreas segmentation. Quantitative evaluation is performed on CT scans of 82 patients in 4-fold cross-validation. We achieve a (mean ± std. dev.) Dice Similarity Coefficient of 78.01 %±8.2 % in testing which significantly outperforms the previous state-of-the-art approach of 71.8 %±10.7 % under the same evaluation criterion. More... »

PAGES

451-459

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
  • 2013. Multi-organ Segmentation Based on Spatially-Divided Probabilistic Atlas from 3D Abdominal CT Images in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • 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 2016

    ISBN

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

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-46723-8_52

    DOI

    http://dx.doi.org/10.1007/978-3-319-46723-8_52

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1084904832


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