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dc.contributor.authorAli, R.en_US
dc.contributor.authorGunduz-Demir, C.en_US
dc.contributor.authorSzilágyi, T.en_US
dc.contributor.authorDurkee, B.en_US
dc.contributor.authorGraves, E.E.en_US
dc.date.accessioned2016-02-08T09:33:44Z
dc.date.available2016-02-08T09:33:44Z
dc.date.issued2013en_US
dc.identifier.issn319155en_US
dc.identifier.urihttp://hdl.handle.net/11693/20713
dc.description.abstractThis paper outlines the first attempt to segment the boundary of preclinical subcutaneous tumours, which are frequently used in cancer research, from micro-computed tomography (microCT) image data. MicroCT images provide low tissue contrast, and the tumour-to-muscle interface is hard to determine, however faint features exist which enable the boundary to be located. These are used as the basis of our semi-automatic segmentation algorithm. Local phase feature detection is used to highlight the faint boundary features, and a level set-based active contour is used to generate smooth contours that fit the sparse boundary features. The algorithm is validated against manually drawn contours and micro-positron emission tomography (microPET) images. When compared against manual expert segmentations, it was consistently able to segment at least 70% of the tumour region (n = 39) in both easy and difficult cases, and over a broad range of tumour volumes. When compared against tumour microPET data, it was able to capture over 80% of the functional microPET volume. Based on these results, we demonstrate the feasibility of subcutaneous tumour segmentation from microCT image data without the assistance of exogenous contrast agents. Our approach is a proof-of-concept that can be used as the foundation for further research, and to facilitate this, the code is open-source and available from www.setuvo.com. © 2013 Institute of Physics and Engineering in Medicine.en_US
dc.language.isoEnglishen_US
dc.source.titlePhysics in Medicine and Biologyen_US
dc.relation.isversionofhttp://dx.doi.org/10.1088/0031-9155/58/22/8007en_US
dc.subjectComputerized tomographyen_US
dc.subjectPositron emission tomographyen_US
dc.subjectTumorsen_US
dc.subjectActive contoursen_US
dc.subjectCancer researchen_US
dc.subjectContrast agenten_US
dc.subjectEmission tomographyen_US
dc.subjectMicrocomputed tomographyen_US
dc.subjectOpen-sourceen_US
dc.subjectProof of concepten_US
dc.subjectSemi-automatic segmentationen_US
dc.subjectImage segmentationen_US
dc.subjectanimalen_US
dc.subjectarticleen_US
dc.subjectautomationen_US
dc.subjectcell transformationen_US
dc.subjecthumanen_US
dc.subjectimage processingen_US
dc.subjectlipomaen_US
dc.subjectmaleen_US
dc.subjectmethodologyen_US
dc.subjectmicro-computed tomographyen_US
dc.subjectmouseen_US
dc.subjectmultimodal imagingen_US
dc.subjectpathologyen_US
dc.subjectpositron emission tomographyen_US
dc.subjectradiographyen_US
dc.subjectscintiscanningen_US
dc.subjectsubcutaneous faten_US
dc.subjecttumor cell lineen_US
dc.subjectAnimalsen_US
dc.subjectAutomationen_US
dc.subjectCell Line, Tumoren_US
dc.subjectCell Transformation, Neoplasticen_US
dc.subjectHumansen_US
dc.subjectImage Processing, Computer-Assisteden_US
dc.subjectMaleen_US
dc.subjectMiceen_US
dc.subjectMultimodal Imagingen_US
dc.subjectNeoplasms, Adipose Tissueen_US
dc.subjectPositron-Emission Tomographyen_US
dc.subjectSubcutaneous Faten_US
dc.subjectX-Ray Microtomographyen_US
dc.titleSemi-automatic segmentation of subcutaneous tumours from micro-computed tomography imagesen_US
dc.typeArticleen_US
dc.departmentDepartment of Radiation Oncology, Stanford University, Stanford, CA, United Statesen_US
dc.departmentDepartment of Computer Engineering, Bilkent University, Ankara, Turkeyen_US
dc.departmentDepartment of Engineering Science, University of Oxford, Oxford, United Kingdomen_US
dc.citation.spage8007en_US
dc.citation.epage8019en_US
dc.citation.volumeNumber58en_US
dc.citation.issueNumber22en_US
dc.identifier.doi10.1088/0031-9155/58/22/8007en_US
dc.publisherInstitute of Physics Publishingen_US


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