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AUTOMATIC SEGMENTATION OF PELVIS FOR BRACHYTHERAPYOF PROSTATE
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-9072-2204
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.ORCID iD: 0000-0003-3352-8330
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.ORCID iD: 0000-0003-0209-498X
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2016 (English)In: Radiation Protection Dosimetry, ISSN 0144-8420, E-ISSN 1742-3406, Vol. 169, no 1-4, 398-404 p.Article in journal (Refereed) Published
Abstract [en]

Advanced model-based iterative reconstruction algorithms in quantitative computed tomography (CT) perform automatic segmentation of tissues to estimate material properties of the imaged object. Compared with conventional methods, these algorithms may improve quality of reconstructed images and accuracy of radiation treatment planning. Automatic segmentation of tissues is, however, a difficult task. The aim of this work was to develop and evaluate an algorithm that automatically segments tissues in CT images of the male pelvis. The newly developed algorithm (MK2014) combines histogram matching, thresholding, region growing, deformable model and atlas-based registration techniques for the segmentation of bones, adipose tissue, prostate and muscles in CT images. Visual inspection of segmented images showed that the algorithm performed well for the five analysed images. The tissues were identified and outlined with accuracy sufficient for the dual-energy iterative reconstruction algorithm whose aim is to improve the accuracy of radiation treatment planning in brachytherapy of the prostate.

Place, publisher, year, edition, pages
2016. Vol. 169, no 1-4, 398-404 p.
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-122978DOI: 10.1093/rpd/ncv461ISI: 000383492100063PubMedID: 26567322OAI: oai:DiVA.org:liu-122978DiVA: diva2:875370
Funder
Swedish Cancer Society, CAN 2012/764Swedish Cancer Society, CAN 2014/691
Note

Funding agencies: Swedish Cancer Foundation [CAN 2012/764, CAN 2014/691]; Medical Faculty, Linkoping University; ALF Grants, Region Ostergotland [LiO-438731]

Available from: 2015-12-01 Created: 2015-12-01 Last updated: 2017-12-01

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Magnusson, MariaSandborg, MichaelAlm Carlsson, GudrunMalusek, Alexandr
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