dc.contributor.advisor Futsæther, Cecilia Marie dc.contributor.advisor Grøndahl, Aurora dc.contributor.advisor Tomic, Oliver dc.contributor.author Mirza, Afreen dc.date.accessioned 2021-01-19T12:02:53Z dc.date.available 2021-01-19T12:02:53Z dc.date.issued 2020 dc.identifier.uri https://hdl.handle.net/11250/2723679 dc.description.abstract The process of delineation of tumors and malignant lymph nodes using medical images is a fundamental part of radiotherapy planning. Still, this process is done manually by radiologists. This process is time-consuming and suffers from inter-observer variability. Hence, there is a need to fully automate this process of delineation to reduce time consumption and inter-observer variability. Deep learning is a division of artificial intelligence that has proven to be useful for the automatic segmentation of medical images with the use of neural networks. We have to follow a systematic procedure as these neural networks require a large number of parameters to be tuned during the delineation process, to guarantee reproducibility. This thesis will present a complete theory of deep learning and a convolutional neural network, for delineating 3D images. The project will use the $deoxys$ framework to implement the V-Net architecture for automatic delineation of gross tumor volume and malignant lymph nodes in the head and neck region. en_US dc.language.iso eng en_US dc.publisher Norwegian University of Life Sciences, Ås en_US dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal * dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no * dc.subject Deep learning en_US dc.subject Medical image analysis en_US dc.title Automated volumetric delineation of cancer tumors on PET/CT images using 3D convolutional neural network (V-Net) en_US dc.type Master thesis en_US dc.subject.nsi VDP::Technology: 500 en_US dc.source.pagenumber 117 en_US dc.description.localcode M-MF en_US
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