Statistical Location Model for Abdominal Organ Localization View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2009

AUTHORS

Jianhua Yao , Ronald M. Summers

ABSTRACT

Initial placement of the models is an essential pre-processing step for model-based organ segmentation. Based on the observation that organs move along with the spine and their relative locations remain relatively stable, we built a statistical location model (SLM) and applied it to abdominal organ localization. The model is a point distribution model which learns the pattern of variability of organ locations relative to the spinal column from a training set of normal individuals. The localization is achieved in three stages: spine alignment, model optimization and location refinement. The SLM is optimized through maximum a posteriori estimation of a probabilistic density model constructed for each organ. Our model includes five organs: liver, left kidney, right kidney, spleen and pancreas. We validated our method on 12 abdominal CTs using leave-one-out experiments. The SLM enabled reduction in the overall localization error from 62.0 +/- 28.5 mm to 5.8 +/- 1.5 mm. Experiments showed that the SLM was robust to the reference model selection. More... »

PAGES

9-17

References to SciGraph publications

  • 2007-12. Segmentation of multiple organs in non-contrast 3D abdominal CT images in INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
  • 1988-01. Snakes: Active contour models in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 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
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-04271-3_2

    DOI

    http://dx.doi.org/10.1007/978-3-642-04271-3_2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1006002119

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/20426090


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