Academic Journal
Προβολή σε EDS

Advancements in prostate zone segmentation: integrating attention mechanisms into the nnU-Net framework /

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Advancements in prostate zone segmentation: integrating attention mechanisms into the nnU-Net framework /
Συγγραφείς: Vaitulevičius, Aleksas, Bernatavičienė, Jolita, Markevičiūtė, Jurgita, Naruševičiūtė, Ieva, Trakymas, Mantas, Treigys, Povilas
Πηγή: Machine learning: science and technology., Bristol : IOPScience, 2024, vol. 5, no. 4, art. no. 045003, p. [1-13]. ; ISSN 2632-2153 ; eISSN 2632-2153
Έτος έκδοσης: 2024
Συλλογή: Vilnius University Virtual Library (VU VL) / Vilniaus universitetas virtuali biblioteka
Θεματικοί όροι: mpMRI, deep learning, artificial intelligence, prostate zone segmentation, computer vision
Περιγραφή: Prostate cancer is one of the most lethal cancers in the world. Early diagnosis is essential for successful treatment of prostate cancer. Segmentation of prostate zones in magnetic resonance images is an important task in the diagnosis of prostate cancer. Currently, the state-of-the-art method for this task is no-new U-Net. In this paper, a method to incorporate the attention U-Net architecture into no-new U-Net is proposed and compared with a classical U-net architecture as research. The experimental results indicate that there is no significant statistical difference between the proposed modification of no-new U-Net with the generalizability of the attention mechanism or the ability to achieve more accurate results. Moreover, two novel workflows are proposed for prostate segmentation, transitional zone segmentation and peripheral zone calculation workflow, and separate models for peripheral zone and transitional zone segmentation workflow. These workflows are compared with a baseline single peripheral zone and transitional zone segmentation model workflow. The experimental results indicate that separate models for peripheral zone and transitional zone segmentation workflow generalizes better than the baseline between data sets of different sources. In peripheral zone segmentation separate models for peripheral zone and transitional zone segmentation workflow achieves 1.9% higher median Dice score coefficient than the baseline workflow when using the attention U-Net architecture and 5.6% higher median Dice score coefficient when using U-Net architecture. Moreover, in transitional zone segmentation separate models for peripheral zone and transitional zone segmentation workflow achieves 0.4% higher median Dice score coefficient than the baseline workflow when using the attention U-Net architecture and 0.7% higher median Dice score coefficient when using U-Net architecture. Meanwhile, prostate segmentation, transitional zone segmentation and peripheral zone calculation workflow generalizes worse than the baseline. ...
Τύπος εγγράφου: article in journal/newspaper
Περιγραφή αρχείου: application/pdf
Γλώσσα: English
Relation: https://epublications.vu.lt/object/elaba:209335806/209335806.pdf; https://repository.vu.lt/VU:ELABAPDB209335806&prefLang=en_US
Διαθεσιμότητα: https://repository.vu.lt/VU:ELABAPDB209335806&prefLang=en_US
Rights: info:eu-repo/semantics/openAccess
Αριθμός Καταχώρησης: edsbas.DB124B80
Βάση Δεδομένων: BASE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://repository.vu.lt/VU:ELABAPDB209335806&prefLang=en_US#
    Name: EDS - BASE (ns324271)
    Category: fullText
    Text: View record from BASE
Header DbId: edsbas
DbLabel: BASE
An: edsbas.DB124B80
RelevancyScore: 945
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 944.983581542969
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Advancements in prostate zone segmentation: integrating attention mechanisms into the nnU-Net framework /
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Vaitulevičius%2C+Aleksas%22">Vaitulevičius, Aleksas</searchLink><br /><searchLink fieldCode="AR" term="%22Bernatavičienė%2C+Jolita%22">Bernatavičienė, Jolita</searchLink><br /><searchLink fieldCode="AR" term="%22Markevičiūtė%2C+Jurgita%22">Markevičiūtė, Jurgita</searchLink><br /><searchLink fieldCode="AR" term="%22Naruševičiūtė%2C+Ieva%22">Naruševičiūtė, Ieva</searchLink><br /><searchLink fieldCode="AR" term="%22Trakymas%2C+Mantas%22">Trakymas, Mantas</searchLink><br /><searchLink fieldCode="AR" term="%22Treigys%2C+Povilas%22">Treigys, Povilas</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Machine learning: science and technology., Bristol : IOPScience, 2024, vol. 5, no. 4, art. no. 045003, p. [1-13]. ; ISSN 2632-2153 ; eISSN 2632-2153
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2024
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: Vilnius University Virtual Library (VU VL) / Vilniaus universitetas virtuali biblioteka
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22mpMRI%22">mpMRI</searchLink><br /><searchLink fieldCode="DE" term="%22deep+learning%22">deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22artificial+intelligence%22">artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22prostate+zone+segmentation%22">prostate zone segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22computer+vision%22">computer vision</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Prostate cancer is one of the most lethal cancers in the world. Early diagnosis is essential for successful treatment of prostate cancer. Segmentation of prostate zones in magnetic resonance images is an important task in the diagnosis of prostate cancer. Currently, the state-of-the-art method for this task is no-new U-Net. In this paper, a method to incorporate the attention U-Net architecture into no-new U-Net is proposed and compared with a classical U-net architecture as research. The experimental results indicate that there is no significant statistical difference between the proposed modification of no-new U-Net with the generalizability of the attention mechanism or the ability to achieve more accurate results. Moreover, two novel workflows are proposed for prostate segmentation, transitional zone segmentation and peripheral zone calculation workflow, and separate models for peripheral zone and transitional zone segmentation workflow. These workflows are compared with a baseline single peripheral zone and transitional zone segmentation model workflow. The experimental results indicate that separate models for peripheral zone and transitional zone segmentation workflow generalizes better than the baseline between data sets of different sources. In peripheral zone segmentation separate models for peripheral zone and transitional zone segmentation workflow achieves 1.9% higher median Dice score coefficient than the baseline workflow when using the attention U-Net architecture and 5.6% higher median Dice score coefficient when using U-Net architecture. Moreover, in transitional zone segmentation separate models for peripheral zone and transitional zone segmentation workflow achieves 0.4% higher median Dice score coefficient than the baseline workflow when using the attention U-Net architecture and 0.7% higher median Dice score coefficient when using U-Net architecture. Meanwhile, prostate segmentation, transitional zone segmentation and peripheral zone calculation workflow generalizes worse than the baseline. ...
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article in journal/newspaper
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: application/pdf
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: https://epublications.vu.lt/object/elaba:209335806/209335806.pdf; https://repository.vu.lt/VU:ELABAPDB209335806&prefLang=en_US
– Name: URL
  Label: Availability
  Group: URL
  Data: https://repository.vu.lt/VU:ELABAPDB209335806&prefLang=en_US
– Name: Copyright
  Label: Rights
  Group: Cpyrght
  Data: info:eu-repo/semantics/openAccess
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsbas.DB124B80
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.DB124B80
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: mpMRI
        Type: general
      – SubjectFull: deep learning
        Type: general
      – SubjectFull: artificial intelligence
        Type: general
      – SubjectFull: prostate zone segmentation
        Type: general
      – SubjectFull: computer vision
        Type: general
    Titles:
      – TitleFull: Advancements in prostate zone segmentation: integrating attention mechanisms into the nnU-Net framework /
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Vaitulevičius, Aleksas
      – PersonEntity:
          Name:
            NameFull: Bernatavičienė, Jolita
      – PersonEntity:
          Name:
            NameFull: Markevičiūtė, Jurgita
      – PersonEntity:
          Name:
            NameFull: Naruševičiūtė, Ieva
      – PersonEntity:
          Name:
            NameFull: Trakymas, Mantas
      – PersonEntity:
          Name:
            NameFull: Treigys, Povilas
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-locals
              Value: edsbas
            – Type: issn-locals
              Value: edsbas.oa
          Titles:
            – TitleFull: Machine learning: science and technology., Bristol : IOPScience, 2024, vol. 5, no. 4, art. no. 045003, p. [1-13]. ; ISSN 2632-2153 ; eISSN 2632-2153
              Type: main
ResultId 1