title: AUTOMATIC SEGMENTATION OF PELVIS FOR BRACHYTHERAPY OF PROSTATE.
abstract: 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.
authors.affiliation: Medical Radiation Physics, Department of Medical and Health Sciences and Center for Medical Image Science and Visualisation, Linköping University, SE-58185 Linköping, Sweden.
authors.forename: M
authors.initials: M
authors.lastname: Kardell
authors.affiliation: Medical Radiation Physics, Department of Medical and Health Sciences and Center for Medical Image Science and Visualisation, Linköping University, SE-58185 Linköping, Sweden Computer Vision Laboratory, Department of Electrical Engineering, Linköping University, SE-58183 Linköping, Sweden.
authors.forename: M
authors.initials: M
authors.lastname: Magnusson
authors.affiliation: Medical Radiation Physics, Department of Medical and Health Sciences and Center for Medical Image Science and Visualisation, Linköping University, SE-58185 Linköping, Sweden.
authors.forename: M
authors.initials: M
authors.lastname: Sandborg
authors.affiliation: Medical Radiation Physics, Department of Medical and Health Sciences and Center for Medical Image Science and Visualisation, Linköping University, SE-58185 Linköping, Sweden.
authors.forename: G
authors.initials: G
authors.lastname: Alm Carlsson
authors.affiliation: Medical Radiation Physics, Department of Medical and Health Sciences and Center for Medical Image Science and Visualisation, Linköping University, SE-58185 Linköping, Sweden.
authors.forename: J
authors.initials: J
authors.lastname: Jeuthe
authors.affiliation: Medical Radiation Physics, Department of Medical and Health Sciences and Center for Medical Image Science and Visualisation, Linköping University, SE-58185 Linköping, Sweden alexandr.malusek@liu.se.
authors.forename: A
authors.initials: A
authors.lastname: Malusek
mesh_terms: D001918:Brachytherapy / Q000379:methods*; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D010388:Pelvis / Q000000981:diagnostic imaging*; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D061089:Radiotherapy, Image-Guided / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods