Academic Journal
View in EDS
PDF Full Text

B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention

Bibliographic Details
Title: B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
Authors: Hamideh Kerdegari, Nhat Tran Huy Phung, Angela McBride, Luigi Pisani, Hao Van Nguyen, Thuy Bich Duong, Reza Razavi, Louise Thwaites, Sophie Yacoub, Alberto Gomez, VITAL Consortium
Source: Applied Sciences, Vol 11, Iss 24, p 11697 (2021)
Publisher Information: MDPI AG, 2021.
Publication Year: 2021
Collection: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
Subject Terms: lung ultrasound (LUS) imaging, b-lines, spatiotemporal attention, classification, video analysis, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
More Details: The presence of B-line artefacts, the main artefact reflecting lung abnormalities in dengue patients, is often assessed using lung ultrasound (LUS) imaging. Inspired by human visual attention that enables us to process videos efficiently by paying attention to where and when it is required, we propose a spatiotemporal attention mechanism for B-line detection in LUS videos. The spatial attention allows the model to focus on the most task relevant parts of the image by learning a saliency map. The temporal attention generates an attention score for each attended frame to identify the most relevant frames from an input video. Our model not only identifies videos where B-lines show, but also localizes, within those videos, B-line related features both spatially and temporally, despite being trained in a weakly-supervised manner. We evaluate our approach on a LUS video dataset collected from severe dengue patients in a resource-limited hospital, assessing the B-line detection rate and the model’s ability to localize discriminative B-line regions spatially and B-line frames temporally. Experimental results demonstrate the efficacy of our approach for classifying B-line videos with an F1 score of up to 83.2% and localizing the most salient B-line regions both spatially and temporally with a correlation coefficient of 0.67 and an IoU of 69.7%, respectively.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2076-3417
Relation: https://www.mdpi.com/2076-3417/11/24/11697; https://doaj.org/toc/2076-3417
DOI: 10.3390/app112411697
Access URL: https://doaj.org/article/290c171af82149e2bb8d7f3330397243
Accession Number: edsdoj.290c171af82149e2bb8d7f3330397243
Database: Directory of Open Access Journals
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHjPtM4BHU3ZchRwgzYmadcigk49r9CVlbU7V5F6lgH7WwEpcUyUFLtvqm-Q60ZKkpgmAAAA4jCB3wYJKoZIhvcNAQcGoIHRMIHOAgEAMIHIBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDMdEHegbU4vx-wwsGwIBEICBmisgZdRQFtaNgWoKE1ioEQ-9pkvi5bRM5Y0PgoRCReKOwXXrB98cocPO1EjMZHiEuTpw24V4yKGBp-f_pvfSeHGs8UXxw-fnoCDDxOPzz-TlXJsvd7egNnJEGYg86k_K-FquE8r2IGvMHo6OX3dJbTqZKXdV5Eao2st1lDDylFcVhf8A0-EO99br7S-BDK5u4ES59aBQ-H6_3NQ=
Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsdoj&genre=article&issn=20763417&ISBN=&volume=11&issue=24&date=20211201&spage=11697&pages=11697-11697&title=Applied Sciences&atitle=B-Line%20Detection%20and%20Localization%20in%20Lung%20Ultrasound%20Videos%20Using%20Spatiotemporal%20Attention&aulast=Hamideh%20Kerdegari&id=DOI:10.3390/app112411697
    Name: Full Text Finder (for New FTF UI) (s8985755)
    Category: fullText
    Text: Find It @ SCU Libraries
    MouseOverText: Find It @ SCU Libraries
  – Url: https://doaj.org/article/290c171af82149e2bb8d7f3330397243
    Name: EDS - DOAJ (s8985755)
    Category: fullText
    Text: View record from DOAJ
    MouseOverText: View record from DOAJ
Header DbId: edsdoj
DbLabel: Directory of Open Access Journals
An: edsdoj.290c171af82149e2bb8d7f3330397243
RelevancyScore: 907
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 906.691040039063
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Hamideh+Kerdegari%22">Hamideh Kerdegari</searchLink><br /><searchLink fieldCode="AR" term="%22Nhat+Tran+Huy+Phung%22">Nhat Tran Huy Phung</searchLink><br /><searchLink fieldCode="AR" term="%22Angela+McBride%22">Angela McBride</searchLink><br /><searchLink fieldCode="AR" term="%22Luigi+Pisani%22">Luigi Pisani</searchLink><br /><searchLink fieldCode="AR" term="%22Hao+Van+Nguyen%22">Hao Van Nguyen</searchLink><br /><searchLink fieldCode="AR" term="%22Thuy+Bich+Duong%22">Thuy Bich Duong</searchLink><br /><searchLink fieldCode="AR" term="%22Reza+Razavi%22">Reza Razavi</searchLink><br /><searchLink fieldCode="AR" term="%22Louise+Thwaites%22">Louise Thwaites</searchLink><br /><searchLink fieldCode="AR" term="%22Sophie+Yacoub%22">Sophie Yacoub</searchLink><br /><searchLink fieldCode="AR" term="%22Alberto+Gomez%22">Alberto Gomez</searchLink><br /><searchLink fieldCode="AR" term="%22VITAL+Consortium%22">VITAL Consortium</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Applied Sciences, Vol 11, Iss 24, p 11697 (2021)
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: MDPI AG, 2021.
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2021
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: LCC:Technology<br />LCC:Engineering (General). Civil engineering (General)<br />LCC:Biology (General)<br />LCC:Physics<br />LCC:Chemistry
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22lung+ultrasound+%28LUS%29+imaging%22">lung ultrasound (LUS) imaging</searchLink><br /><searchLink fieldCode="DE" term="%22b-lines%22">b-lines</searchLink><br /><searchLink fieldCode="DE" term="%22spatiotemporal+attention%22">spatiotemporal attention</searchLink><br /><searchLink fieldCode="DE" term="%22classification%22">classification</searchLink><br /><searchLink fieldCode="DE" term="%22video+analysis%22">video analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Technology%22">Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+%28General%29%2E+Civil+engineering+%28General%29%22">Engineering (General). Civil engineering (General)</searchLink><br /><searchLink fieldCode="DE" term="%22TA1-2040%22">TA1-2040</searchLink><br /><searchLink fieldCode="DE" term="%22Biology+%28General%29%22">Biology (General)</searchLink><br /><searchLink fieldCode="DE" term="%22QH301-705%2E5%22">QH301-705.5</searchLink><br /><searchLink fieldCode="DE" term="%22Physics%22">Physics</searchLink><br /><searchLink fieldCode="DE" term="%22QC1-999%22">QC1-999</searchLink><br /><searchLink fieldCode="DE" term="%22Chemistry%22">Chemistry</searchLink><br /><searchLink fieldCode="DE" term="%22QD1-999%22">QD1-999</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: The presence of B-line artefacts, the main artefact reflecting lung abnormalities in dengue patients, is often assessed using lung ultrasound (LUS) imaging. Inspired by human visual attention that enables us to process videos efficiently by paying attention to where and when it is required, we propose a spatiotemporal attention mechanism for B-line detection in LUS videos. The spatial attention allows the model to focus on the most task relevant parts of the image by learning a saliency map. The temporal attention generates an attention score for each attended frame to identify the most relevant frames from an input video. Our model not only identifies videos where B-lines show, but also localizes, within those videos, B-line related features both spatially and temporally, despite being trained in a weakly-supervised manner. We evaluate our approach on a LUS video dataset collected from severe dengue patients in a resource-limited hospital, assessing the B-line detection rate and the model’s ability to localize discriminative B-line regions spatially and B-line frames temporally. Experimental results demonstrate the efficacy of our approach for classifying B-line videos with an F1 score of up to 83.2% and localizing the most salient B-line regions both spatially and temporally with a correlation coefficient of 0.67 and an IoU of 69.7%, respectively.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: electronic resource
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2076-3417
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: https://www.mdpi.com/2076-3417/11/24/11697; https://doaj.org/toc/2076-3417
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.3390/app112411697
– Name: URL
  Label: Access URL
  Group: URL
  Data: <link linkTarget="URL" linkTerm="https://doaj.org/article/290c171af82149e2bb8d7f3330397243" linkWindow="_blank">https://doaj.org/article/290c171af82149e2bb8d7f3330397243</link>
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsdoj.290c171af82149e2bb8d7f3330397243
PLink https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsdoj&AN=edsdoj.290c171af82149e2bb8d7f3330397243
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/app112411697
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: 11697
    Subjects:
      – SubjectFull: lung ultrasound (LUS) imaging
        Type: general
      – SubjectFull: b-lines
        Type: general
      – SubjectFull: spatiotemporal attention
        Type: general
      – SubjectFull: classification
        Type: general
      – SubjectFull: video analysis
        Type: general
      – SubjectFull: Technology
        Type: general
      – SubjectFull: Engineering (General). Civil engineering (General)
        Type: general
      – SubjectFull: TA1-2040
        Type: general
      – SubjectFull: Biology (General)
        Type: general
      – SubjectFull: QH301-705.5
        Type: general
      – SubjectFull: Physics
        Type: general
      – SubjectFull: QC1-999
        Type: general
      – SubjectFull: Chemistry
        Type: general
      – SubjectFull: QD1-999
        Type: general
    Titles:
      – TitleFull: B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Hamideh Kerdegari
      – PersonEntity:
          Name:
            NameFull: Nhat Tran Huy Phung
      – PersonEntity:
          Name:
            NameFull: Angela McBride
      – PersonEntity:
          Name:
            NameFull: Luigi Pisani
      – PersonEntity:
          Name:
            NameFull: Hao Van Nguyen
      – PersonEntity:
          Name:
            NameFull: Thuy Bich Duong
      – PersonEntity:
          Name:
            NameFull: Reza Razavi
      – PersonEntity:
          Name:
            NameFull: Louise Thwaites
      – PersonEntity:
          Name:
            NameFull: Sophie Yacoub
      – PersonEntity:
          Name:
            NameFull: Alberto Gomez
      – PersonEntity:
          Name:
            NameFull: VITAL Consortium
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 20763417
          Numbering:
            – Type: volume
              Value: 11
            – Type: issue
              Value: 24
          Titles:
            – TitleFull: Applied Sciences
              Type: main
ResultId 1