Improving cervical neoplasia diagnosis via novel in vivo imaging technologies and deep learning algorithms
department or school: Applied Science, Faculty of
department or school: Biomedical Engineering, School of
data provider: DSpace
degree campus: UBCV
creator: Sheikhzadeh, Fahime
issued: 2018-04-27T21:31:06Z
issued: 2018
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Two directions are explored for improving the current cervical cancer diagnosis procedure. The first investigates the future deployment of in vivo confocal imaging in the clinic, for detecting precancerous tissues, and the second proposes an algorithm for automatic interpretation of histology images (acquired by light microscopy). We acquired i) confocal microscopy images of cervical biopsies taken from 50 patients, at different tissue depths and ii) histology images of different sections cut from each biopsy. From the confocal images, we identified four features that carry enough information relevant to cell morphology and tissue architecture. We demonstrated that the relevant information in these features is comparable to that extracted from the same features in histology images. This implies that we can obtain the relevant information from confocal imaging, without having to cut a biopsy from the patient’s cervix. We then studied the confocal images and determined the grade lesion of every biopsy and found that confocal imaging resulted in less false positives than the diagnosis given by the gynecologist (based on the appearance of the cervix under colposcopy). Utilizing confocal microscopy technology in the clinic would thus decrease the number of unnecessary biopsies. We then developed a deep learning algorithm that automatically and quantitatively assesses HPV contaminated and proliferating cells in histology images of biopsy sections. The automatic assessment of this procedure is important as it plays a significant role in differentiating between disease grades but forms a challenging and complex task and demands a large amount of time when performed manually by a pathologist. We demonstrated that this algorithm could help the pathologists to differentiate between different grades of cervical precancerous tissues. Our results are also more reproducible compared to other methods (like color deconvolution) that are widely being used in the field of digital path…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/65676
note: IMPROVING CERVICAL NEOPLASIA DIAGNOSIS VIA NOVEL IN VIVO IMAGING TECHNOLOGIES AND DEEP LEARNING ALGORITHMS by Fahime Sheikhzadeh B.Sc. Amirkabir Univeristy of Technology, 2007 M.Sc. Amirkabir Univeristy of Technology, 2009 M.A.Sc., The University of British Columbia, 2012 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (BIOMEDICAL ENGINEERING) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2018 © Fahime Sheikhzadeh, 2018 ii Abstract Two directions are explored for improving the current cervical cancer diagnosis procedure. The first investigates the future deployment of in vivo confocal imaging in the clinic, for detecting precancerous tissues, and the second proposes an algorithm for automatic interpretation of histology images (acquired by light microscopy). We acquired i) confocal microscopy images of cervical biopsies taken from 50 patients, at different tissue depths and ii) histology images of different sections cut from each biopsy. From the confocal images, we identified four features that carry enough information relevant to cell morphology and tissue architecture. We demonstrated that the relevant information in these features is comparable to that extracted from the same features in histology images. This implies that we can obtain the relevant information from confocal imaging, without having to cut a biopsy from the patient’s cervix. We then studied the confocal images and determined the grade lesion of every biopsy and found that confocal imaging resulted in less false positives than the diagnosis given by the gynecologist (based on the appearance of the cervix under colposcopy). Utilizing confocal microscopy technology in the clinic would thus decrease the number of unnecessary biopsies. We then developed a deep learning algorithm that automatically and quantitatively assesses HPV contaminated and proliferating cells in histology images of bio…
has type: Thesis/Dissertation
date issued: 2018-09
is shown at: 10.14288/1.0366058
language: eng
degree discipline: Biomedical 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/2429/65676