A machine learning framework for temporal enhanced ultrasound guided prostate cancer diagnostics
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Azizi, Shekoofeh
issued: 2018-07-03T15:32:34Z
issued: 2018
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: The ultimate diagnosis of prostate cancer involves histopathology analysis of tissue samples obtained through prostate biopsy, guided by either transrectal ultrasound (TRUS), or fusion of TRUS with multi-parametric magnetic resonance imaging. Appropriate clinical management of prostate cancer requires accurate detection and assessment of the grade of the disease and its extent. Despite recent advancements in prostate cancer diagnosis, accurate characterization of aggressive lesions from indolent ones is an open problem and requires refinement. Temporal Enhanced Ultrasound (TeUS) has been proposed as a new paradigm for tissue characterization. TeUS involves analysis of a sequence of ultrasound radio frequency (RF) or Brightness (B)-mode data using a machine learning approach. The overarching objective of this dissertation is to improve the accuracy of detecting prostate cancer, specifically the aggressive forms of the disease and to develop a TeUS-augmented prostate biopsy system. Towards full-filling this objective, this dissertation makes the following contributions: 1) Several machine learning techniques are developed and evaluated to automatically analyze the spectral and temporal aspect of backscattered ultrasound signals from the prostate tissue, and to detect the presence of cancer; 2) a patient-specific biopsy targeting approach is proposed that displays near real-time cancer likelihood maps on B-mode ultrasound images augmenting their information; and 3) the latent representations of TeUS, as learned by the proposed machine learning models, are investigated to derive insights about tissue dependent features residing in TeUS and their physical interpretation. A data set consisting of biopsy targets in mp-MRI-TRUS fusion-biopsies with 255 biopsy cores from 157 subjects was used to generate and evaluate the proposed techniques. Clinical histopathology of the biopsy cores was used as the gold-standard. Results demonstrated that TeUS is effective in differentiati…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/66385
note: A Machine Learning Framework forTemporal Enhanced UltrasoundGuided Prostate Cancer DiagnosticsbyShekoofeh AziziB.Sc., Isfahan University of Technology, 2011M.Sc., Isfahan University of Technology, 2013A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Electrical and Computer Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)June 2018c© Shekoofeh Azizi 2018The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:A Machine Learning Framework for Temporal Enhanced Ultra-sound Guided Prostate Cancer Diagnosticssubmitted by Shekoofeh Azizi in partial fulfillment of the requirementsfor the degree of Doctor of Philosophy in Electrical and ComputerEngineering.Examining Committee:Purang Abolmaesumi, Electrical and Computer EngineeringSupervisorShahriar Mirabbasi, Electrical and Computer EngineeringUniversity ExaminerRoger Tam, Electrical and Computer EngineeringUniversity ExaminerZahra Moussavi, Electrical and Computer EngineeringExternal ExaminerAdditional Supervisory Committee Members:Robert Rohling, Electrical and Computer EngineeringSupervisory Committee MemberSeptimiu E. Salcudean, Electrical and Computer EngineeringSupervisory Committee MemberiiAbstractThe ultimate diagnosis of prostate cancer involves histopathology analysis oftissue samples obtained through prostate biopsy, guided by either transrectalultrasound (TRUS), or fusion of TRUS with multi-parametric magneticresonance imaging. Appropriate clinical management of prostate cancerrequires accurate detection and assessment of the grade of the disease and itsextent. Despite recent advancements in prostate cancer diagnosis, accuratecharacterization of aggressive lesions from indolent ones is an open problemand requires refinement.Temporal Enhanced Ultrasound (TeUS) has been proposed as a newparadigm for tissue char…
has type: Thesis/Dissertation
date issued: 2018-09
is shown at: 10.14288/1.0368786
language: eng
degree discipline: Electrical and Computer Engineering
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
type: Text
identifier URI: http://hdl.handle.net/2429/66385