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dc.contributor.advisorNailon, Williamen
dc.contributor.authorCheng, Kunen
dc.date.accessioned2017-07-18T12:59:09Z
dc.date.available2017-07-18T12:59:09Z
dc.date.issued2016-07-02
dc.identifier.urihttp://hdl.handle.net/1842/22893
dc.description.abstractRadiotherapy is the most widely used treatment for cancer, with 4 out of 10 cancer patients receiving radiotherapy as part of their treatment. The delineation of gross tumour volume (GTV) is crucial in the treatment of radiotherapy. An automatic contouring system would be beneficial in radiotherapy planning in order to generate objective, accurate and reproducible GTV contours. Image guided radiotherapy (IGRT) acquires patient images just before treatment delivery to allow any necessary positional correction. Consequently, real-time contouring system provides an opportunity to adopt radiotherapy on the treatment day. In this thesis, freely deformable models (FDM) and shape constrained deformable models (SCDMs) were used to automatically delineate the GTV for brain cancer and prostate cancer. Level set method (LSM) is a typical FDM which was used to contour glioma on brain MRI. A series of low level image segmentation methodologies are cascaded to form a case-wise fully automatic initialisation pipeline for the level set function. Dice similarity coefficients (DSCs) were used to evaluate the contours. Results shown a good agreement between clinical contours and LSM contours, in 93% of cases the DSCs was found to be between 60% and 80%. The second significant contribution is a novel development to the active shape model (ASM), a profile feature was selected from pre-computed texture features by minimising the Mahalanobis distance (MD) to obtain the most distinct feature for each landmark, instead of conventional image intensity. A new group-wise registration scheme was applied to solve the correspondence definition within the training data. This ASM model was used to delineated prostate GTV on CT. DSCs for this case was found between 0.75 and 0.91 with the mean DSC 0.81. The last contribution is a fully automatic active appearance model (AAM) which captures image appearance near the GTV boundary. The image appearance of inner GTV was discarded to spare the potential disruption caused by brachytherapy seeds or gold markers. This model outperforms conventional AAM at the prostate base and apex region by involving surround organs. The overall mean DSC for this case is 0.85.en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.relation.hasversionCheng K, Montgomery D, Feng Y, Steel R, Liao H, McLaren DB, Erridge SC, McLaughlin S, Nailon WH. Identifying Radiotherapy Target Volumes in Brain Cancer by Image Analysis. Healthcare Technology Letters, Volume 2, Issue 5, October 2015, p. 123 â˘A ¸S 12.en
dc.relation.hasversionFeng Y, Welsh D, McDonald K, Carruthers L, Cheng K, Montgomery D, Lawrence J, Argyle DJ, McLaughlin S, McLaren DB, H. Nailon WH. Identifying the Dominant Prostate Cancer Focal Lesion using Image Analysis and Planning of a Simultaneous Integrated SABR Boost. Acta Oncologica, 54.9 (2015): 1543-1550.en
dc.relation.hasversionCheng K, Feng Y, Montgomery D, Steel R, Liao H, McLaren DB, McLaughlin S, Nailon WH. Active Shape Models for Prostate Cancer Planning with Optimized Features. In Proc of the International Society for Optics and Photonics (SPIE) 2014, San Diego.en
dc.relation.hasversionFeng Y, Cheng K, Lawrence Y, Forrest L, McLaren DB, McLaughlin S, Argyle DJ, Nailon WH. A new image registration framework for improving radiotherapy delivery. In Proc of Symposium on Small Animal RadioTherapy, 3rd-5th March 2013, Maastricht, the Netherlands.en
dc.relation.hasversionMontgomery D, Campbell, S, Cheng K, Feng Y, Murchison J, McLaren DB, Yong AW, Ritchie G, McLaren DB, Erridge SC, McLaughlin S, Nailon WH. Predicting the occurrence of radiation induced pneumonitis by texture analysis of CT images from lung cancer patients. In: MICCAI Workshop on Pulmonary Image Analysis, MICCAI 2013.en
dc.relation.hasversionCheng K, Feng Y, Steel R, McLaren DB, Erridge SC, McLaughlin S, Nailon WH. Level set identification of radiotherapy target volumes on magnetic resonance images. In Proc of 5th of International Conference on Advances in Medical Signal and Information Processing (MEDSIP). Liverpool, UK, 5th-7th July 2012.en
dc.relation.hasversionFeng Y, Cheng K, Tian Y, McLaren DB, McLaughlin S, Argyle D, Nailon WH. Scale invariant image registration for focal treatment of prostate cancer by radiotherapy. In Proc of 5th International Conference on Advances in Medical Signal and Information Processing (MEDSIP). Liverpool, UK, 5th-7th July 2012en
dc.subjectbrain canceren
dc.subjectprostate canceren
dc.subjectLSMen
dc.subjectASMen
dc.subjectAAMen
dc.titleDeformable models for adaptive radiotherapy planningen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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