Anatomical Segmentation of CT images for Radiation Therapy planning using Deep Learning

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Laaksonen, Hannu
dc.contributor.author Schreier, Jan
dc.date.accessioned 2018-10-17T08:06:52Z
dc.date.available 2018-10-17T08:06:52Z
dc.date.issued 2018-10-08
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/34379
dc.description.abstract Radiation therapy is one of the key cancer treatment options. To avoid adverse effect in tissue surrounding the tumor, the treatment plan needs to be based on accurate anatomical models of the patient. In this thesis, an automatic segmentation solution is constructed for the female breast, the female pelvis and the male pelvis using deep learning. The deep neural networks applied performed as well as the current state of the art networks while improving inference speed by a factor of 15 to 45. The speed increase was gained through processing the whole 3D image at once. The segmentations done by clinicians usually take several hours, whereas the automatic segmentation can be done in less than a second. Therefore, the automatic segmentation provides options for adaptive treatment planning. en
dc.format.extent 74+10
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Anatomical Segmentation of CT images for Radiation Therapy planning using Deep Learning en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword image segmentation en
dc.subject.keyword deep learning en
dc.subject.keyword artificial intelligence en
dc.subject.keyword radiation therapy en
dc.identifier.urn URN:NBN:fi:aalto-201810175454
dc.programme.major Biomedical Engineering fi
dc.programme.mcode SCI3059 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Parkkonen, Lauri
dc.programme Master’s Programme in Life Science Technologies fi
local.aalto.electroniconly yes
local.aalto.openaccess yes


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