Organ Pose Distribution Model and an MAP Framework for Automated Abdominal Multi-organ Localization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Xiaofeng Liu , Marius George Linguraru , Jianhua Yao , Ronald M. Summers

ABSTRACT

Abdominal organ localization is required as an initialization step for most automated abdominal organ analysis tasks, i.e. segmentation, registration, and computer aided-diagnosis. Automated abdominal organ localization is difficult because of the large variability of organ shapes, similar appearances of different organs in images, and organs in close proximity to each other. Previous methods predicted only the organ locations, but not the full organ poses including additionally sizes and orientations. Thus they were often not accurate enough to initialize other image analysis tasks. In this work we proposed a maximum a posteriori (MAP) framework to estimate the poses of multiple abdominal organs from non-contrast CT images. A novel organ pose distribution model is proposed to model the organ poses and limit the search space. Additionally the method uses probabilistic atlases for organ shapes, and Gaussian mixture models for organ intensity profile. An MAP problem is then formulated and solved for organ poses. The method was applied for the localization of liver, left and right kidneys, spleen, and pancreas, and showed promising results, especially on liver and spleen (with mean location and orientation errors under 5.3 mm and 7 degrees respectively). More... »

PAGES

393-402

References to SciGraph publications

  • 2008. Construction of Hierarchical Multi-Organ Statistical Atlases and Their Application to Multi-Organ Segmentation from CT Images in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2008
  • 2006. Active Shape Models for a Fully Automated 3D Segmentation of the Liver – An Evaluation on Clinical Data in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2006
  • 2009. Liver Segmentation Using Automatically Defined Patient Specific B-Spline Surface Models in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2009
  • 2009. Statistical Location Model for Abdominal Organ Localization in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2009
  • 2007-12. Segmentation of multiple organs in non-contrast 3D abdominal CT images in INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
  • 2004. Independent Component Analysis of Four-Phase Abdominal CT Images in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2004
  • Book

    TITLE

    Medical Imaging and Augmented Reality

    ISBN

    978-3-642-15698-4
    978-3-642-15699-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15699-1_41

    DOI

    http://dx.doi.org/10.1007/978-3-642-15699-1_41

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1039626618


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