Development of MR image analysis with machine learning to enhance T2W images with luminal water imaging for prostate cancer
department or school: Science, Faculty of
department or school: Physics and Astronomy, Department of
data provider: DSpace
degree campus: UBCV
creator: Ip, Candice Jia Xin
issued: 2022-08-30T20:48:40Z
issued: 2022
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Luminal water imaging (LWI) is a novel quantitative MRI technique developed to aid with prostate cancer diagnosis. The results of LWI have shown significance in relating its derived parameters to the Gleason score, indicating its usefulness with prostate cancer grading. However, LWI parameters cannot show anatomical information on which radiologists rely for diagnosis. To address the lack of anatomic detail and to allow for LWI to become clinically feasible, a method for enhancing anatomical T2-Weighted (T2W) images with luminal water parametric maps is proposed. In addition to enhancing images, a U-Net model is implemented to automatically segment the prostate region and peripheral zone in T2W images. Results indicate that LWI-enhanced T2W images can provide better image contrast when compared to T2W images alone. Automatic segmentation shows a Dice coefficient score of 91.93% and 75.05% accuracy for the whole-prostate and peripheral zone segmentation, respectively. With automatic segmentation and an LWI enhancement routine, anatomical T2W images can be quickly processed to provide better cancer image diagnostics to radiologists in a clinical setting.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/82608
note: Development of MR Image Analysiswith Machine Learning to EnhanceT2W Images with Luminal WaterImaging for Prostate CancerbyCandice Jia Xin IpB.A.Sc., The University of British Columbia, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Physics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)August 2022© Candice Jia Xin Ip 2022The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the thesis entitled:Development of MR Image Analysis with Machine Learning to EnhanceT2W Images with Luminal Water Imaging for Prostate Cancersubmitted by Candice Jia-Xin Ip in partial fulfillment of the requirements forthe degree of Master of Sciencein PhysicsExamining Committee:Piotr Kozlowski, Associate Professor, Department of Physics and Astronomy, UBCSupervisorStefan Reinsberg, Associate Professor, Department of Physics and Astronomy, UBCSupervisory Committee MemberiiAbstractLuminal water imaging (LWI) is a novel quantitative MRI technique developed to aid withprostate cancer diagnosis. The results of LWI have shown significance in relating its derivedparameters to the Gleason score, indicating its usefulness with prostate cancer grading.However, LWI parameters cannot show anatomical information on which radiologists relyfor diagnosis. To address the lack of anatomic detail and to allow for LWI to becomeclinically feasible, a method for enhancing anatomical T2-Weighted (T2W) images withluminal water parametric maps is proposed. In addition to enhancing images, a U-Netmodel is implemented to automatically segment the prostate region and peripheral zone inT2W images. Results indicate that LWI-enhanced T2W images can provide better imagecontrast when compared to T2W images alone. Automatic segmentation shows a Dicecoefficient score of 91.93 % and 75.05 % accuracy for the whole-prostate and peripheralzone segmentat…
has type: Thesis/Dissertation
date issued: 2022-11
is shown at: 10.14288/1.0418450
language: eng
degree discipline: Physics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Kozlowski, Piotr
type: Text
identifier URI: http://hdl.handle.net/2429/82608