Fractional Labelmap Representation of Anatomical Structures

dc.contributor.authorSunderland, Kyleen
dc.contributor.departmentComputingen
dc.contributor.supervisorFichtinger, Gaboren
dc.date.accessioned2017-09-09T22:44:15Z
dc.date.available2017-09-09T22:44:15Z
dc.degree.grantorQueen's University at Kingstonen
dc.description.abstractINTRODUCTION: In medical imaging software, structures are often represented using image volumes known as labelmaps. Conversion of structures to labelmap representations results in a loss of information which impacts medical imaging algorithms, including those in radiation therapy (RT) treatment planning. RT treatment planning systems are used to optimize radiation delivery to structures, which are represented as planar contours. Errors from conversion affect metrics such as dose volume histograms (DVHs) that are the primary metric for RT plan optimization. The goal of this thesis is to develop fractional labelmaps as a structure representation that preserves more structural information and reduces conversion errors. METHODS: The effect of voxelization on treatment planning metrics was tested by comparing DVHs calculated using varying voxel sizes. An algorithm was implemented that triangulates planar contours to closed surface mesh and handles features such as branching, keyhole contours, and end-capping. Closed surfaces were converted into fractional labelmap representations, where each voxel represents occupancy between 0% and 100%. Existing segmentation methods were modified to allow fractional labelmaps to be edited directly. RESULTS: Algorithms were implemented in the open-source medical imaging platform 3D Slicer, and the SlicerRT toolkit. Voxel size was found to affect DVH accuracy for structures with small features or in regions with a high dose gradient. The planar contour to closed surface triangulation algorithm produced qualitatively good surfaces. Fractional labelmaps from closed surfaces were found to be up to 19.1% better at representing structure volume than binary labelmaps, with an average improvement of 6.8%. DVHs calculated from fractional labelmaps were found to be more accurate than DVHs calculated using binary labelmaps. Fractional segmentation methods were found to create good quality segmentations. CONCLUSION: Labelmap voxel size was found to be a contributing factor for DVH accuracy. Accurate conversion algorithms were implemented for planar contour to closed surface and closed surface to fractional labelmap conversions. When used for structure representation and DVH calculation, fractional labelmaps were more accurate than binary labelmaps at the same resolution. Fractional labelmaps were found to be an effective tool for structure representation in radiotherapy that could be expanded to other use cases.en
dc.description.degreeM.Sc.en
dc.identifier.urihttp://hdl.handle.net/1974/22677
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectFractional Labelmapen
dc.subjectStructure Representationen
dc.subjectRadiation Therapyen
dc.subjectDose Volume Histogramen
dc.subjectVoxelizationen
dc.subjectSegmentationen
dc.titleFractional Labelmap Representation of Anatomical Structuresen
dc.typethesisen
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