Deep Learning Architectures and Techniques for Multi-organ Segmentation

Deep learning architectures used for automatic multi-organ segmentation in the medical field have gained increased attention in the last years as the results and achievements outweighed the older techniques. Due to improvements in the computer hardware and the development of specialized network desi...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of advanced computer science & applications Ročník 12; číslo 1
Hlavní autoři: Ogrean, Valentin, Dorobantiu, Alexandru, Brad, Remus
Médium: Journal Article
Jazyk:angličtina
Vydáno: West Yorkshire Science and Information (SAI) Organization Limited 2021
Témata:
Artificial neural networks
Deep learning
Generative adversarial networks
Neural networks
Recurrent neural networks
Segmentation
ISSN:2158-107X, 2156-5570
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract Deep learning architectures used for automatic multi-organ segmentation in the medical field have gained increased attention in the last years as the results and achievements outweighed the older techniques. Due to improvements in the computer hardware and the development of specialized network designs, deep learning segmentation presents exciting developments and opportunities also for future research. Therefore, we have compiled a review of the most interesting deep learning architectures applicable to medical multi-organ segmentation. We have summarized over 50 contributions, most of which are more recent than 3 years. The papers were grouped into three categories based on the architecture: “Convolutional Neural Networks” (CNNs), “Fully Convolutional Neural Networks” (FCNs) and hybrid architectures that combine more designs - including “Generative Adversarial Networks” (GANs) or “Recurrent Neural Networks” (RNNs). Afterwards we present the most used multi-organ datasets, and we finalize by making a general discussion of current shortcomings and future potential research paths.
AbstractList Deep learning architectures used for automatic multi-organ segmentation in the medical field have gained increased attention in the last years as the results and achievements outweighed the older techniques. Due to improvements in the computer hardware and the development of specialized network designs, deep learning segmentation presents exciting developments and opportunities also for future research. Therefore, we have compiled a review of the most interesting deep learning architectures applicable to medical multi-organ segmentation. We have summarized over 50 contributions, most of which are more recent than 3 years. The papers were grouped into three categories based on the architecture: “Convolutional Neural Networks” (CNNs), “Fully Convolutional Neural Networks” (FCNs) and hybrid architectures that combine more designs - including “Generative Adversarial Networks” (GANs) or “Recurrent Neural Networks” (RNNs). Afterwards we present the most used multi-organ datasets, and we finalize by making a general discussion of current shortcomings and future potential research paths.
Author Ogrean, Valentin
Dorobantiu, Alexandru
Brad, Remus
Author_xml – sequence: 1
  givenname: Valentin
  surname: Ogrean
  fullname: Ogrean, Valentin
– sequence: 2
  givenname: Alexandru
  surname: Dorobantiu
  fullname: Dorobantiu, Alexandru
– sequence: 3
  givenname: Remus
  surname: Brad
  fullname: Brad, Remus
BookMark eNotkM1PAjEQxRuDiYj8Bx428bw4_WR73KAiBuMBTLw1tR1gCXSx2z3437N8vMu8l7zMTH73pBfqgIQ8UhhRIZV-nn2Uk0U5YsDoCCgDCuKG9BmVKpdyDL2zL3IK4587MmyaLXTimqmC98n0BfGQzdHGUIV1Vka3qRK61EZsMht8tkS3CdVf28VVHbPPdpeqvI5rG7IFrvcYkk1VHR7I7cruGhxe54B8v70uJ-_5_Gs6m5Tz3LGxSDlqxZwVWmuvuUZw8MuYQ67AOkDOlXUI1jvpeMGpd7qAwgvHvBCUeoZ8QJ4uew-xPj2VzLZuY-hOGqakpFQLwbuWuLRcrJsm4socYrW38d9QMGdq5kLNnKiZKzV-BMooYY4
ContentType Journal Article
Copyright 2021. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2021. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID AAYXX
CITATION
3V.
7XB
8FE
8FG
8FK
8G5
ABUWG
AFKRA
ARAPS
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
GNUQQ
GUQSH
HCIFZ
JQ2
K7-
M2O
MBDVC
P5Z
P62
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
Q9U
DOI 10.14569/IJACSA.2021.0120104
DatabaseName CrossRef
ProQuest Central (Corporate)
ProQuest Central (purchase pre-March 2016)
ProQuest SciTech Collection
ProQuest Technology Collection
ProQuest Central (Alumni) (purchase pre-March 2016)
Research Library (Alumni)
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
Advanced Technologies & Computer Science Collection
ProQuest Central Essentials
ProQuest Central
Technology collection
ProQuest One Community College
ProQuest Central
ProQuest Central Student
ProQuest Research Library
SciTech Premium Collection
ProQuest Computer Science Collection
Computer Science Database
Research Library
Research Library (Corporate)
Advanced Technologies & Aerospace Database
ProQuest Advanced Technologies & Aerospace Collection
ProQuest Central Premium
ProQuest One Academic (New)
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
ProQuest Central Basic
DatabaseTitle CrossRef
Publicly Available Content Database
Research Library Prep
Computer Science Database
ProQuest Central Student
Technology Collection
ProQuest One Academic Middle East (New)
ProQuest Advanced Technologies & Aerospace Collection
ProQuest Central Essentials
ProQuest Computer Science Collection
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
Research Library (Alumni Edition)
ProQuest Central China
ProQuest Central
ProQuest One Applied & Life Sciences
ProQuest Central Korea
ProQuest Research Library
ProQuest Central (New)
Advanced Technologies & Aerospace Collection
ProQuest Central Basic
ProQuest One Academic Eastern Edition
ProQuest Technology Collection
ProQuest SciTech Collection
Advanced Technologies & Aerospace Database
ProQuest One Academic UKI Edition
ProQuest One Academic
ProQuest One Academic (New)
ProQuest Central (Alumni)
DatabaseTitleList Publicly Available Content Database
Database_xml – sequence: 1
  dbid: PIMPY
  name: Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISSN 2156-5570
ExternalDocumentID 10_14569_IJACSA_2021_0120104
GroupedDBID .DC
5VS
8G5
AAYXX
ABUWG
ADMLS
AFFHD
AFKRA
ALMA_UNASSIGNED_HOLDINGS
ARAPS
AZQEC
BENPR
BGLVJ
CCPQU
CITATION
DWQXO
EBS
EJD
GNUQQ
GUQSH
HCIFZ
K7-
KQ8
M2O
OK1
PHGZM
PHGZT
PIMPY
PQGLB
RNS
3V.
7XB
8FE
8FG
8FK
JQ2
MBDVC
P62
PKEHL
PQEST
PQQKQ
PQUKI
PRINS
Q9U
ID FETCH-LOGICAL-c274t-e962ca4999d939e0c0b22ce360ac0e336ace0adc5c3831dc9808d4c2d4411d2e3
IEDL.DBID K7-
ISICitedReferencesCount 1
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000621697400004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2158-107X
IngestDate Sun Nov 09 07:09:50 EST 2025
Sat Nov 29 02:26:00 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed false
IsScholarly true
Issue 1
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c274t-e962ca4999d939e0c0b22ce360ac0e336ace0adc5c3831dc9808d4c2d4411d2e3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
OpenAccessLink https://www.proquest.com/docview/2655119443?pq-origsite=%requestingapplication%
PQID 2655119443
PQPubID 5444811
ParticipantIDs proquest_journals_2655119443
crossref_primary_10_14569_IJACSA_2021_0120104
PublicationCentury 2000
PublicationDate 2021-00-00
20210101
PublicationDateYYYYMMDD 2021-01-01
PublicationDate_xml – year: 2021
  text: 2021-00-00
PublicationDecade 2020
PublicationPlace West Yorkshire
PublicationPlace_xml – name: West Yorkshire
PublicationTitle International journal of advanced computer science & applications
PublicationYear 2021
Publisher Science and Information (SAI) Organization Limited
Publisher_xml – name: Science and Information (SAI) Organization Limited
SSID ssj0000392683
Score 2.136812
Snippet Deep learning architectures used for automatic multi-organ segmentation in the medical field have gained increased attention in the last years as the results...
SourceID proquest
crossref
SourceType Aggregation Database
Index Database
SubjectTerms Artificial neural networks
Deep learning
Generative adversarial networks
Neural networks
Recurrent neural networks
Segmentation
Title Deep Learning Architectures and Techniques for Multi-organ Segmentation
URI https://www.proquest.com/docview/2655119443
Volume 12
WOSCitedRecordID wos000621697400004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVPQU
  databaseName: Advanced Technologies & Aerospace Database
  customDbUrl:
  eissn: 2156-5570
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000392683
  issn: 2158-107X
  databaseCode: P5Z
  dateStart: 20100101
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/hightechjournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Computer Science Database
  customDbUrl:
  eissn: 2156-5570
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000392683
  issn: 2158-107X
  databaseCode: K7-
  dateStart: 20100101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/compscijour
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central
  customDbUrl:
  eissn: 2156-5570
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000392683
  issn: 2158-107X
  databaseCode: BENPR
  dateStart: 20100101
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Publicly Available Content Database
  customDbUrl:
  eissn: 2156-5570
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000392683
  issn: 2158-107X
  databaseCode: PIMPY
  dateStart: 20100101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Research Library
  customDbUrl:
  eissn: 2156-5570
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000392683
  issn: 2158-107X
  databaseCode: M2O
  dateStart: 20100101
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/pqrl
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV07T8MwELagMLDwRpSXPLAaHDsPe0LhUSjQEvGQCkvk2E7FQFqawu_HTpwCCwuLF0uRdWff3XeXuw-AQ59lERfMRzrjOfJJLpHgEUaSUZHnhDOlq0bh26jfZ4MBT1zCrXS_VTY2sTLUaiRtjvyYhIEtefk-PRm_I8saZaurjkJjHix4hHj2nt9EaJZjwcb5h9UkTuPY7BTTaOC650zYwI-71_HZQ2wwIvGObA-p59jaZt7pt3GuPE5n5b9nXQXLLtaEcX051sCcLtbBSsPjAN2z3gCX51qPoZu0OoTxj9pCCUWh4GMz6LWEJsaFVdMuqvig4IMevrn2pWITPHUuHs-ukCNYQNKA0SnSPCRSWMyjOOUaS5wRIjUNsZBYUxoKqbFQMpAGx3pKcoaZ8iVRJobyFNF0C7SKUaG3AfSEH8lIRcLo18c0ZzRjmueKBCyTBsO1AWoEm47rORqpxR9WEWmtiNQqInWKaIO9RrSpe1Vl-i3Xnb-3d8GS_VidKtkDrenkQ--DRfk5fS0nB2Dh9KKf3B9Ul8WsPXJn1iR4MTtJt5c8fwENH8iT
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LTtwwFL3iJcGm0AICSsGLdmlwbE9sL1A1ggLTmY4qMUizC47tIBaEgQxF_BTfWDtxoN2wY8E6kqXkXN2Xc84B-MplLpSWHLtcFZjTwmCtBMFGMl0UVEnraqLwQAyHcjxWv2fgqeXChN8q25xYJ2p7Y8KOfJ-mnXDlxTn7PrnFwTUq3K62FhpNWPTd44Mf2aqD3pHH9xulxz9Gh6c4ugpg4yewKXYqpUaHRt8qphwxJKfUOJYSbYhjLNXGEW1Nx_jhLbFGSSItN9T6xiGx1DF_7izMcyZF0OrvC_y80yG-2Uhr5U9fSINqqhhHtp5vU9R-72f38KzrZ1Ka7AXOahLd4Z6r4f_FoK5wx8vv7duswIfYS6NuE_wfYcaVn2C59alAMW2twsmRcxMUlWQvUfefu5MK6dKiUStkWyHfw6OalIxrvyt05i6vIz2rXIPzN3mddZgrb0q3ASjRXBhhhfbxywkrJMulU4WlHZkbP6NuAm6BzCaNTkgW5qsAfNYAnwXgswj8Jmy3UGYxa1TZC45brz_ehcXT0a9BNugN-59hKRzcrIW2YW56d---wIL5M72q7nbqAEVw8dao_wVkaSEB
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep+Learning+Architectures+and+Techniques+for+Multi-organ+Segmentation&rft.jtitle=International+journal+of+advanced+computer+science+%26+applications&rft.au=Ogrean%2C+Valentin&rft.au=Dorobantiu%2C+Alexandru&rft.au=Brad%2C+Remus&rft.date=2021&rft.issn=2158-107X&rft.eissn=2156-5570&rft.volume=12&rft.issue=1&rft_id=info:doi/10.14569%2FIJACSA.2021.0120104&rft.externalDBID=n%2Fa&rft.externalDocID=10_14569_IJACSA_2021_0120104
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2158-107X&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2158-107X&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2158-107X&client=summon