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 |