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Automatic multi–organ segmentation using fast model based level set method and hierarchical shape priors
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-0442-3524
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.ORCID iD: 0000-0002-7750-1917
2014 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

An automatic multi-organ segmentation pipeline is presented. The segmentation starts with stripping the body of skin and subcutaneous fat using threshold-based level-set methods. After registering the image to be processed against a standard subject picked from the training datasets, a series of model-based level set segmentation operations is carried out guided by hierarchical shape priors. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, starting with ventral cavity, and then divided into thoracic cavity and abdominopelvic cavity. The third level contains the individual organs such as liver, spleen and kidneys. The segmentation is performed in a top-down fashion, where major structures are segmented rst, and their location information is then passed down to the lower level to initialize the segmentation, while boundary information from higher-level structures also constrains the segmentation of the lower-level structures. In our preliminary experiments, the proposed method yielded a Dice coecient around 90% for most major thoracic and abdominal organs in both contrastenhanced CT and non-enhanced datasets, while the average running time for segmenting ten organs was about 10 minutes.

Place, publisher, year, edition, pages
2014.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-115738OAI: oai:DiVA.org:liu-115738DiVA: diva2:796205
Conference
ISBI CEUR Workshop, 2014
Available from: 2015-03-18 Created: 2015-03-18 Last updated: 2015-04-01

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Wang, ChunliangSmedby, Örjan

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Wang, ChunliangSmedby, Örjan
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Division of Radiological SciencesFaculty of Health SciencesCenter for Medical Image Science and Visualization (CMIV)Department of Radiology in Linköping
Probability Theory and Statistics

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf