View in EDS

CervSpineNet: a hybrid deep learning-based approach for the segmentation of cervical spinous processes.

Saved in:
Bibliographic Details
Title: CervSpineNet: a hybrid deep learning-based approach for the segmentation of cervical spinous processes.
Authors: Sawant JS; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States., Moukheiber L; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.; Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, United States., Nair A; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.; School of Computer Science, Georgia Institute of Technology, Atlanta, GA, United States., Mahajan A; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.; School of Computer Science, Georgia Institute of Technology, Atlanta, GA, United States., Byun J; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States., Pichaimani I; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States., Yoon ST; Department of Orthopedic Surgery, Emory University, Atlanta, GA, United States., Martin CT; Department of Orthopedic Surgery, University of Minnesota, Minneapolis, MN, United States., Mitchell CS; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.; Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, United States.
Source: Frontiers in bioengineering and biotechnology [Front Bioeng Biotechnol] 2026 Jan 19; Vol. 13, pp. 1733689. Date of Electronic Publication: 2026 Jan 19 (Print Publication: 2025).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Media S.A Country of Publication: Switzerland NLM ID: 101632513 Publication Model: eCollection Cited Medium: Print ISSN: 2296-4185 (Print) Linking ISSN: 22964185 NLM ISO Abbreviation: Front Bioeng Biotechnol Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
FullText Text:
  Availability: 0
Header DbId: mdl
DbLabel: MEDLINE Ultimate
An: 41635798
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: CervSpineNet: a hybrid deep learning-based approach for the segmentation of cervical spinous processes.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Sawant+JS%22">Sawant JS</searchLink>; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.<br /><searchLink fieldCode="AU" term="%22Moukheiber+L%22">Moukheiber L</searchLink>; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.; Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, United States.<br /><searchLink fieldCode="AU" term="%22Nair+A%22">Nair A</searchLink>; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.; School of Computer Science, Georgia Institute of Technology, Atlanta, GA, United States.<br /><searchLink fieldCode="AU" term="%22Mahajan+A%22">Mahajan A</searchLink>; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.; School of Computer Science, Georgia Institute of Technology, Atlanta, GA, United States.<br /><searchLink fieldCode="AU" term="%22Byun+J%22">Byun J</searchLink>; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.<br /><searchLink fieldCode="AU" term="%22Pichaimani+I%22">Pichaimani I</searchLink>; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.<br /><searchLink fieldCode="AU" term="%22Yoon+ST%22">Yoon ST</searchLink>; Department of Orthopedic Surgery, Emory University, Atlanta, GA, United States.<br /><searchLink fieldCode="AU" term="%22Martin+CT%22">Martin CT</searchLink>; Department of Orthopedic Surgery, University of Minnesota, Minneapolis, MN, United States.<br /><searchLink fieldCode="AU" term="%22Mitchell+CS%22">Mitchell CS</searchLink>; Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.; Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, United States.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22101632513%22">Frontiers in bioengineering and biotechnology</searchLink> [Front Bioeng Biotechnol] 2026 Jan 19; Vol. 13, pp. 1733689. <i>Date of Electronic Publication: </i>2026 Jan 19 (<i>Print Publication: </i>2025).
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Frontiers+Media+S%2EA%22">Frontiers Media S.A </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>101632513 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Print <i>ISSN: </i>2296-4185 (Print) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2222964185%22">22964185 </searchLink><i>NLM ISO Abbreviation: </i>Front Bioeng Biotechnol <i>Subsets: </i>PubMed not MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=41635798
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3389/fbioe.2025.1733689
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: 1733689
    Titles:
      – TitleFull: CervSpineNet: a hybrid deep learning-based approach for the segmentation of cervical spinous processes.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Sawant JS
      – PersonEntity:
          Name:
            NameFull: Moukheiber L
      – PersonEntity:
          Name:
            NameFull: Nair A
      – PersonEntity:
          Name:
            NameFull: Mahajan A
      – PersonEntity:
          Name:
            NameFull: Byun J
      – PersonEntity:
          Name:
            NameFull: Pichaimani I
      – PersonEntity:
          Name:
            NameFull: Yoon ST
      – PersonEntity:
          Name:
            NameFull: Martin CT
      – PersonEntity:
          Name:
            NameFull: Mitchell CS
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 19
              M: 01
              Text: 2026 Jan 19
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 2296-4185
          Numbering:
            – Type: volume
              Value: 13
          Titles:
            – TitleFull: Frontiers in bioengineering and biotechnology
              Type: main
ResultId 1