Image-guided therapies have the potential to improve the accuracy of treating liver cancer. In order to register intraoperative with preoperative liver images, joint segmentation and registration methods require fast segmentation of matching vessel centerlines. The algorithm presented in this thesis solves this problem by tracking the centerlines using ridge and cross-section information, and uses knowledge of the patient’s vasculature in the preoperative image to ensure correspondence. The algorithm was tested on three MR images of healthy volunteers and one CT image of a patient with liver cancer. Results show that in the context of join segmentation registration, if the registration error is less than 2.0mm, the average segmentation error is 0.73-1.68mm, with 88-100% of the vessels having an error less than a voxel length. For registration error less than 4.6mm, the average segmentation error is 1.17-2.11mm, with 79-98% of the vessels having an error less than a voxel length.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/43359 |
| Date | 11 December 2013 |
| Creators | Zaheer, Sameer |
| Contributors | Drake, James, Huang, Xishi |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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