Evaluating and Improving 4D-CT Image Segmentation for Lung Cancer Radiotherapy

Lung cancer is a high-incidence disease with low survival despite surgical advances and concurrent chemo-radiotherapy strategies. Image-guided radiotherapy provides for treatment measures, however, significant challenges exist for imaging, treatment planning, and delivery of radiation due to the influence of respiratory motion. 4D-CT imaging is capable of improving image quality of thoracic target volumes influenced by respiratory motion. 4D-CT-based treatment planning strategies requires highly accurate anatomical segmentation of tumour volumes for radiotherapy treatment plan optimization. Variable segmentation of tumour volumes significantly contributes to uncertainty in radiotherapy planning due to a lack of knowledge regarding the exact shape of the lesion and difficulty in quantifying variability. As image-segmentation is one of the earliest tasks in the radiotherapy process, inherent geometric uncertainties affect subsequent stages, potentially jeopardizing patient outcomes. Thus, this work assesses and suggests strategies for mitigation of segmentation-related geometric uncertainties in 4D-CT-based lung cancer radiotherapy at pre- and post-treatment planning stages.

Name thesis:degree_name

M Sc

Discipline thesis:degree_discipline

Medical Biophysics

Grantor dc:publisher

The University of Western Ontario

Year dc:date.issued

2013

Author dc:creator

• Martin, Spencer P

Advisors dc:contributor.advisor

• Stewart Gaede
• George Rodrigues

• Lung cancer
• 4D-CT
• Image segmentation
• Radiotherapy
• STAPLE

Language dc:language.iso

en_ca

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14721/36878

OAI identifier oai:identifier

oai:uwo.scholaris.ca:20.500.14721/36878