Anatomical segmentation of CT images for radiation therapy planning using deep learning

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master_Schreier_Jan_2018.pdf (3.55 MB)

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Perustieteiden korkeakoulu | Master's thesis
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Date

2018-10-08

Department

Major/Subject

Biomedical Engineering

Mcode

SCI3059

Degree programme

Master’s Programme in Life Science Technologies

Language

en

Pages

74+10

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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.

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Supervisor

Parkkonen, Lauri

Thesis advisor

Laaksonen, Hannu

Keywords

image segmentation, deep learning, artificial intelligence, radiation therapy

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https://urn.fi/URN:NBN:fi:aalto-201810175454

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[dipl] Perustieteiden korkeakoulu / SCI