Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Amal Farag , Le Lu , Holger R. Roth , Jiamin Liu , Evrim Turkbey , Ronald M. Summers

ABSTRACT

Accurateautomaticdetectionandsegmentationof abdominal organs from CT images is important for quantitative and qualitative organ tissue analysis, detection of pathologies, surgical assistance as well as computer-aided diagnosis (CAD). In general, the large variability of organ locations, the spatial interaction between organs that appear similar in medical scans and orientation and size variations are among the major challenges of organ segmentation. The pancreas poses these challenges in addition to its flexibility which allows for the shape of the tissue to vastly change. In this chapter, we present a fully automated bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans. The method is a four-stage system based on a hierarchical cascade of information propagation by classifying image patches at different resolutions and cascading (segments) superpixels. System components consist of the following: (1) decomposing CT slice images as a set of disjoint boundary-preserving superpixels; (2) computing pancreas class probability maps via dense patch labeling; (3) classifying superpixels by pooling both intensity and probability features to form empirical statistics in cascaded random forest frameworks; and (4) simple connectivity based post-processing. Evaluation of the approach is conducted on a database of 80 manually segmented CT volumes in sixfold cross validation. Our achieved results are comparable, or better to the state-of-the-art methods (evaluated by “leave-one-patient-out”), with a Dice coefficient of \(70.7\%\) and Jaccard Index of \(57.9\%\). The computational efficiency of the proposed approach is drastically improved in the order of 6–8 min, compared to other methods of \({\ge }10\) hours per testing case. More... »

PAGES

279-302

References to SciGraph publications

  • 2008. Simultaneous Detection and Registration for Ileo-Cecal Valve Detection in 3D CT Colonography in COMPUTER VISION – ECCV 2008
  • 2004-09. Efficient Graph-Based Image Segmentation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2012. Semantic Segmentation with Second-Order Pooling in COMPUTER VISION – ECCV 2012
  • 2004-05. Robust Real-Time Face Detection in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2015. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION -- MICCAI 2015
  • 2010-01. Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography in INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
  • 2012. Abdominal Multi-Organ Segmentation of CT Images Based on Hierarchical Spatial Modeling of Organ Interrelations in ABDOMINAL IMAGING. COMPUTATIONAL AND CLINICAL APPLICATIONS
  • 2014. Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation in MEDICAL COMPUTER VISION. LARGE DATA IN MEDICAL IMAGING
  • 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
  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2013. Multi-organ Segmentation Based on Spatially-Divided Probabilistic Atlas from 3D Abdominal CT Images in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • 2016. Hierarchical Multi-Organ Segmentation Without Registration in 3D Abdominal CT Images in MEDICAL COMPUTER VISION: ALGORITHMS FOR BIG DATA
  • 2012. Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2012
  • 2012. Multi-organ Abdominal CT Segmentation Using Hierarchically Weighted Subject-Specific Atlases in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2012
  • 2011. Automatic Multi-organ Segmentation Using Learning-Based Segmentation and Level Set Optimization in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2011
  • 2014. A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans in ABDOMINAL IMAGING. COMPUTATIONAL AND CLINICAL APPLICATIONS
  • 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2015
  • Book

    TITLE

    Deep Learning and Convolutional Neural Networks for Medical Image Computing

    ISBN

    978-3-319-42998-4
    978-3-319-42999-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-42999-1_16

    DOI

    http://dx.doi.org/10.1007/978-3-319-42999-1_16

    DIMENSIONS

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    N-Triples is a line-based linked data format ideal for batch operations.

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    Turtle is a human-readable linked data format.

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    RDF/XML is a standard XML format for linked data.

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