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Optimizing the accuracy of tumor segmentation in pet for radiotherapy planning using blind deconvolution method

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dc.contributor Ph.D. Program in Biomedical Engineering.
dc.contributor.advisor Güveniş, Albert.
dc.contributor.author Koç, Alpaslan.
dc.date.accessioned 2023-03-16T13:17:08Z
dc.date.available 2023-03-16T13:17:08Z
dc.date.issued 2019.
dc.identifier.other BM 2019 K64 PhD
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/19111
dc.description.abstract Tumor segmentation accuracy greatly affects the effectiveness of radiotherapy procedures. Maximizing the segmentation accuracy has high medical significance in order to deliver the highest radiation dose to the target volume while protecting the healthy tissues. This dissertation aims to present an optimized method to minimize errors in the automated segmentation of tumors in PET images. Blind deconvolution wasimplementedinaregionofinterestencompassingthetumorwithaniterationnumber determined from Contrast-to-Noise Ratios. The images were resampled. Several automaticsegmentationalgorithmsweretestedonthreedatasets: phantom, simulated geometric lesions inserted in real images, and simulated clinical images with real heterogeneous tumors for which ground truth was known. The volumes of the tumors were 0.49-26.34cc, 0.64-1.52cc, and40.38-203.84ccrespectivelyforthethreedatasets. The widely available software tools MATLAB, MIPAV, and ITK-SNAP were used. With theuseoftheactivecontourwithclassificationtechnique,themeanerrorswerereduced from 95.85% to 3.37%, from 815.63% to 17.45%, and from 32.61% to 6.80% for all the lesionsofthephantomdataset,thesimulateddataset,andthelargelesionsoftheclinicalPETdatasetrespectively. Thecomputationaltimewasreducedbyafactorofmore than 10 by the use of region-of-interest-based deconvolution. Contrast-to-Noise Ratio and Region-of-Interest based deconvolution have the potential to improve delineation accuracyfordifferentsizesofhomogeneousandheterogeneoustumors. Improvementis veryimportantforsmallertumors. Thealgorithmmayprovidereducedcomputational time with respect to full deconvolution and can be implemented using widely available software tools.|Keywords : PET, medical image segmentation, image restoration, blind deconvolution, radiotherapy planning.
dc.format.extent 30 cm.
dc.publisher Thesis (Ph.D.)-Bogazici University. Institute of Biomedical Engineering, 2019.
dc.subject.lcsh Radiotherapy.
dc.subject.lcsh Tomography, Emission.
dc.subject.lcsh Imaging systems in medicine.
dc.title Optimizing the accuracy of tumor segmentation in pet for radiotherapy planning using blind deconvolution method
dc.format.pages xvii, 76 leaves ;


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