Staff View
Improvements in cardiac segmentation for cross-modality domain adaptation

Descriptive

TitleInfo
Title
Improvements in cardiac segmentation for cross-modality domain adaptation
Name (type = personal)
NamePart (type = family)
Gindra
NamePart (type = given)
Rushin Hitesh
NamePart (type = date)
1996-
DisplayForm
Rushin Hitesh Gindra
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Metaxas
NamePart (type = given)
Dimitris N
DisplayForm
Dimitris N Metaxas
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Stratos
NamePart (type = given)
Karl
DisplayForm
Karl Stratos
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Michmizos
NamePart (type = given)
Konstantinos P
DisplayForm
Konstantinos P Michmizos
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal 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
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2020
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2020-05
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2020
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
In medical image computing, the problem of heterogeneous domain shift is quite common and severe, causing many deep convolutional networks to under-perform on various imaging modalities. Retraining the network is difficult since annotating the new domain data is prohibitively expensive, specifically in medical areas that require expertise. While recent works show approaches to tackle this problem using unsupervised domain adaptation, segmentation modules in such methods can be improved vastly. Our implementation provides a segmentation improvement on the current state-of-the-art framework, Synergistic Image and Feature Adaptation(SIFA). We revisit atrous spatial pyramid pooling while using convolutional features as well as image features for multi-scale object segmentation. We have validated the effectiveness of the improvement on the framework using the challenging application of cross-modality segmentation of cardiac structures. To demonstrate the robustness of the module, extensive experiments have been performed on Long-Axis(MMWHS) cross-modal cardiac segmentation tasks.
Subject (authority = local)
Topic
Unsupervised domain adaptation
Subject (authority = LCSH)
Topic
Diagnostic imaging
Subject (authority = RUETD)
Topic
Computer Science
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10852
PhysicalDescription
Form (authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (viii, 26 pages) : illustrations
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/t3-yrc6-rh77
Genre (authority = ExL-Esploro)
ETD graduate
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Gindra
GivenName
Rushin
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2020-04-27 21:27:00
AssociatedEntity
Name
Rushin Gindra
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.
RightsEvent
Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2020-05-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2020-11-30
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after November 30th, 2020.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
CreatingApplication
Version
1.5
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2020-05-04T16:32:02
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2020-05-04T16:32:02
ApplicationName
pdfTeX-1.40.20
Back to the top
Version 8.5.3
Rutgers University Libraries - Copyright ©2024