DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2015

AUTHORS

Holger R. Roth , Le Lu , Amal Farag , Hoo-Chang Shin , Jiamin Liu , Evrim B. Turkbey , Ronald M. Summers

ABSTRACT

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 previous segmentation methods from achieving high accuracies, especially compared to other organs such as the liver, heart or kidneys. In this paper, we present a probabilistic bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans, using multi-level deep convolutional networks (ConvNets). We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i.e. superpixels. We first present a dense labeling of local image patches via P-ConvNet and nearest neighbor fusion. Then we describe a regional ConvNet (R 1−ConvNet) that samples a set of bounding boxes around each image superpixel at different scales of contexts in a “zoom-out” fashion. Our ConvNets learn to assign class probabilities for each superpixel region of being pancreas. Last, we study a stacked R 2−ConvNet leveraging the joint space of CT intensities and the P−ConvNet dense probability maps. Both 3D Gaussian smoothing and 2D conditional random fields are exploited as structured predictions for post-processing. We evaluate on CT images of patients in 4-fold cross-validation. We achieve a Dice Similarity Coefficient of 83.6±6.3% in training and 71.8±10.7% in testing. More... »

PAGES

556-564

References to SciGraph publications

  • 2006-11. Graph Cuts and Efficient N-D Image Segmentation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2014. Geodesic Patch-Based Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2014
  • 2015-01. The Pascal Visual Object Classes Challenge: A Retrospective in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2013. Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • 2013. Multi-organ Segmentation Based on Spatially-Divided Probabilistic Atlas from 3D Abdominal CT Images in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • 2014. A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2014
  • 2014. A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans in ABDOMINAL IMAGING. COMPUTATIONAL AND CLINICAL APPLICATIONS
  • 2014. Databases Theory and Applications, 25th Australasian Database Conference, ADC 2014, Brisbane, QLD, Australia, July 14-16, 2014. Proceedings in NONE
  • Book

    TITLE

    Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015

    ISBN

    978-3-319-24552-2
    978-3-319-24553-9

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-24553-9_68

    DOI

    http://dx.doi.org/10.1007/978-3-319-24553-9_68

    DIMENSIONS

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