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Automatic Multi-organ Segmentation in Nonenhanced CT Datasets Using 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). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.ORCID iD: 0000-0002-7750-1917
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
2014 (English)In: Pattern Recognition (ICPR), 2014 22nd International Conference on, IEEE Computer Society, 2014, 3327-3332 p.Conference paper, Published paper (Refereed)
Abstract [en]

An automatic multi-organ segmentation method using hierarchical-shape-prior guided level sets is proposed. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, so that major structures with less population variety are at the top and smaller structures with higher irregularities are linked at a lower level. The segmentation is performed in a top-down fashion, where major structures are first segmented with higher confidence, and their location information is then passed down to the lower level to initialize the segmentation, while boundary information from higher-level structures also provides extra cues to guide the segmentation of the lower-level structures. The proposed method was combined with a novel coherent propagating level set method, which is capable to detect local convergence and skip calculation in those parts, therefore significantly reducing computation time. Preliminary experiment results on a small number of clinical datasets are encouraging, the proposed method yielded a Dice coefficient above 90% for most major organs within a reasonable processing time without any user intervention.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014. 3327-3332 p.
Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-115587DOI: 10.1109/ICPR.2014.574ISI: 000359818003077ISBN: 9781479952090 (electronic)OAI: oai:DiVA.org:liu-115587DiVA: diva2:795763
Conference
22nd International Conference on Pattern Recognition (ICPR) 2014, Stockholm Sweden
Available from: 2015-03-17 Created: 2015-03-17 Last updated: 2017-03-07

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Smedby, ÖrjanWang, Chunliang

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Division of Radiological SciencesFaculty of Health SciencesCenter for Medical Image Science and Visualization (CMIV)Department of Radiology in Linköping
Radiology, Nuclear Medicine and Medical Imaging

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