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Automatic multi–organ segmentation using fast model based level set method and hierarchical shape priors
Center for Medical Imaging Science and Visualization(CMIV), Linköping University, Linköping, Sweden.ORCID iD: 0000-0002-0442-3524
Department of Medical and Health Sciences (IMH), Linköping University, Linköping, Sweden.ORCID iD: 0000-0002-7750-1917
2014 (English)In: Proceedings of the VISCERAL Organ Segmentation and Landmark Detection Challenge, co-located with IEEE International Symposium on Biomedical Imaging (ISBI 2014), Beijing, China, May 1, 2014, CEUR-WS , 2014, Vol. 1194, p. 25-31Conference paper, Published paper (Refereed)
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 first, 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 coeffcient 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
CEUR-WS , 2014. Vol. 1194, p. 25-31
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 1194
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-258855Scopus ID: 2-s2.0-84925279448OAI: oai:DiVA.org:kth-258855DiVA, id: diva2:1350255
Conference
VISCERAL Organ Segmentation and Landmark Detection Challenge, VISCERAL 2014 - Co-located with IEEE International Symposium on Biomedical Imaging, ISBI 2014; Beijing; China; 1 May 2014
Note

QC 20190916

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-09-16Bibliographically approved

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Smedby, Örjan

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CiteExportLink to record
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