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Computer aided analysis of prostate histopathology images

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TitleInfo
Title
Computer aided analysis of prostate histopathology images
Name (type = personal)
NamePart (type = family)
Ren
NamePart (type = given)
Jian
NamePart (type = date)
1991-
DisplayForm
Jian Ren
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Foran
NamePart (type = given)
David J
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David J Foran
Affiliation
Advisory Committee
Role
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chair
Name (type = personal)
NamePart (type = family)
Hacihaliloglu
NamePart (type = given)
Ilker
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Ilker Hacihaliloglu
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Parashar
NamePart (type = given)
Manish
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Manish Parashar
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Singer
NamePart (type = given)
Eric
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Eric Singer
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
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Text
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theses
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2019
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
Prostate cancer is the most common non-skin related cancer affecting 1 in 7 men in the United States. Treatment of patients with prostate cancer remains a difficult decision-making process that requires physicians to balance clinical benefits, life expectancy, morbidities, and potential side effects. Gleason scores have been shown to serve as the best predictors of prostate cancer outcomes. In spite of progress made in trying to standardize the grading process, there still remains approximately a 30% grading discrepancy between the score rendered by general pathologists and those provided by experts while reviewing needle biopsies for Gleason pattern 3 and 4, which accounts for more than 70% of daily prostate tissue slides at most institutions. Therefore, we present computational imaging methods for prostate gland analysis which we will utilize to develop an automated reliable computer-aided Gleason grading system. The inspiration for the project starts from the fact that prostate adenocarcinoma is diagnosed by recognizing certain histology fields clinically. Recently, the Gleason grading criteria used to perform Gleason grading was updated to allow more accurate stratification and higher prognostic discrimination as compared to the traditional grading system.

In this thesis work, we have gone beyond Gleason score analysis by introducing survival model assessment to predict patient outcomes. Using whole-slide images (WSIs) generated from biopsy tissues from radical prostatectomy surgical specimens, we utilize deep learning approaches to discover the most promising computational image biomarkers. The proposed method differs from existing survival analysis studies that use individual patches or manually designed protocols to select a set of patches. In contrast to those approaches, we develop an end-to-end methodology to learn from patches that are analyzed sequentially while preserving their inter-spatial relationships within the WSIs. We build the automatically cropped patches from a WSI as a sequence and use the recurrent neural network to generate a salient representative computational biomarker for the WSI.

Automatic and accurate Gleason grading of histopathology tissue slides is crucial for reliable prostate cancer diagnosis, treatment, and prognosis. Usually, histopathology tissue slides from different institutions show heterogeneous appearances because of variation in tissue preparation and staining procedures, thus the predictable model learned from one domain may not be applicable to a new domain, directly. Here we propose to adopt unsupervised domain adaptation to transfer the discriminative knowledge obtained from the source domain to the target domain without requiring labeling of images at the target domain. The adaptation is achieved through adversarial training to find an invariant feature space along with the proposed Siamese architecture on the target domain to add the regularization that is appropriate for the whole-slide images. We validate the method on two prostate cancer datasets and obtain significant classification improvement of Gleason score as compared with the baseline models.

Finally, we explore the possibility of utilizing cluster computing infrastructure to speed up the analysis. The nuclei detection algorithm that was previously reported extremely reliable in terms of accuracy, but suffered from the fact that performance took an inordinate amount of time to run on a single machine. We have addressed this challenge and present here a parallel nuclei detection algorithm that has been implemented on CometCloud.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (authority = LCSH)
Topic
Prostate -- Imaging
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10209
PhysicalDescription
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application/pdf
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text/xml
Extent
1 online resource (xvii, 123 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-r75x-vc44
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Ren
GivenName
Jian
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-09-02 21:23:50
AssociatedEntity
Name
Jian Ren
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2019-09-10T17:48:46
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-09-10T17:48:46
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