Automatic Detection of Cervical Vertebral Landmarks for Fluoroscopic Joint Motion Analysis

Ida Marie Groth Jakobsen, Maciej Plocharski

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Resumé

Real-time motion assessment of the cervical spine provides an understanding of its mechanics and reveals abnormalities in its motion patterns. In this paper we propose a vertebral segmentation approach to automatically identify the vertebral landmarks for cervical joint motion analysis using videofluoroscopy. Our method matches a template to the vertebral bodies, identified using two parallel segmentation approaches, and validates the results through comparison to manually annotated landmarks. The algorithm identified the vertebral corners with an average detection error under five pixels in the C3–C6 vertebrae, with the lowest average error of 1.65 pixels in C4. C7 yielded the largest average error of 6.15 pixels. No significant difference was observed between the intervertebral angles computed using the manually annotated and automatically detected landmarks ($$p>0.05$$). The proposed method does not require large amounts of data for training, eliminates the necessity for manual annotations, and allows for real-time intervertebral motion analysis of the cervical spine.
OriginalsprogEngelsk
TitelImage Analysis : 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings
RedaktørerMichael Felsberg, Per-Erik Forssén, Jonas Unger, Ida-Maria Sintorn
Antal sider12
ForlagSpringer
Publikationsdato2019
Sider209-220
ISBN (Trykt)978-3-030-20204-0
ISBN (Elektronisk)978-3-030-20205-7
DOI
StatusUdgivet - 2019
Begivenhed21st Scandinavian Conference on Image Analysis, SCIA 2019 - Norrköping, Sverige
Varighed: 11 jun. 201913 jun. 2019

Konference

Konference21st Scandinavian Conference on Image Analysis, SCIA 2019
LandSverige
ByNorrköping
Periode11/06/201913/06/2019
NavnLecture Notes in Computer Science
Vol/bind11482
ISSN0302-9743

Emneord

    Citer dette

    Jakobsen, I. M. G., & Plocharski, M. (2019). Automatic Detection of Cervical Vertebral Landmarks for Fluoroscopic Joint Motion Analysis. I M. Felsberg, P-E. Forssén, J. Unger, & I-M. Sintorn (red.), Image Analysis : 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings (s. 209-220). Springer. Lecture Notes in Computer Science, Bind. 11482 https://doi.org/10.1007/978-3-030-20205-7_18
    Jakobsen, Ida Marie Groth ; Plocharski, Maciej. / Automatic Detection of Cervical Vertebral Landmarks for Fluoroscopic Joint Motion Analysis. Image Analysis : 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings. red. / Michael Felsberg ; Per-Erik Forssén ; Jonas Unger ; Ida-Maria Sintorn. Springer, 2019. s. 209-220 (Lecture Notes in Computer Science, Bind 11482).
    @inproceedings{335ed233acc3495cb891eb731f93c606,
    title = "Automatic Detection of Cervical Vertebral Landmarks for Fluoroscopic Joint Motion Analysis",
    abstract = "Real-time motion assessment of the cervical spine provides an understanding of its mechanics and reveals abnormalities in its motion patterns. In this paper we propose a vertebral segmentation approach to automatically identify the vertebral landmarks for cervical joint motion analysis using videofluoroscopy. Our method matches a template to the vertebral bodies, identified using two parallel segmentation approaches, and validates the results through comparison to manually annotated landmarks. The algorithm identified the vertebral corners with an average detection error under five pixels in the C3–C6 vertebrae, with the lowest average error of 1.65 pixels in C4. C7 yielded the largest average error of 6.15 pixels. No significant difference was observed between the intervertebral angles computed using the manually annotated and automatically detected landmarks ($$p>0.05$$). The proposed method does not require large amounts of data for training, eliminates the necessity for manual annotations, and allows for real-time intervertebral motion analysis of the cervical spine.",
    keywords = "Automatic detection, Cervical spine, Segmentation, Vertebral landmarks, Videofluoroscopy",
    author = "Jakobsen, {Ida Marie Groth} and Maciej Plocharski",
    year = "2019",
    doi = "10.1007/978-3-030-20205-7_18",
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    isbn = "978-3-030-20204-0",
    pages = "209--220",
    editor = "Michael Felsberg and Per-Erik Forss{\'e}n and Jonas Unger and Ida-Maria Sintorn",
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    }

    Jakobsen, IMG & Plocharski, M 2019, Automatic Detection of Cervical Vertebral Landmarks for Fluoroscopic Joint Motion Analysis. i M Felsberg, P-E Forssén, J Unger & I-M Sintorn (red), Image Analysis : 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings. Springer, Lecture Notes in Computer Science, bind 11482, s. 209-220, Norrköping, Sverige, 11/06/2019. https://doi.org/10.1007/978-3-030-20205-7_18

    Automatic Detection of Cervical Vertebral Landmarks for Fluoroscopic Joint Motion Analysis. / Jakobsen, Ida Marie Groth; Plocharski, Maciej.

    Image Analysis : 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings. red. / Michael Felsberg; Per-Erik Forssén; Jonas Unger; Ida-Maria Sintorn. Springer, 2019. s. 209-220.

    Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

    TY - GEN

    T1 - Automatic Detection of Cervical Vertebral Landmarks for Fluoroscopic Joint Motion Analysis

    AU - Jakobsen, Ida Marie Groth

    AU - Plocharski, Maciej

    PY - 2019

    Y1 - 2019

    N2 - Real-time motion assessment of the cervical spine provides an understanding of its mechanics and reveals abnormalities in its motion patterns. In this paper we propose a vertebral segmentation approach to automatically identify the vertebral landmarks for cervical joint motion analysis using videofluoroscopy. Our method matches a template to the vertebral bodies, identified using two parallel segmentation approaches, and validates the results through comparison to manually annotated landmarks. The algorithm identified the vertebral corners with an average detection error under five pixels in the C3–C6 vertebrae, with the lowest average error of 1.65 pixels in C4. C7 yielded the largest average error of 6.15 pixels. No significant difference was observed between the intervertebral angles computed using the manually annotated and automatically detected landmarks ($$p>0.05$$). The proposed method does not require large amounts of data for training, eliminates the necessity for manual annotations, and allows for real-time intervertebral motion analysis of the cervical spine.

    AB - Real-time motion assessment of the cervical spine provides an understanding of its mechanics and reveals abnormalities in its motion patterns. In this paper we propose a vertebral segmentation approach to automatically identify the vertebral landmarks for cervical joint motion analysis using videofluoroscopy. Our method matches a template to the vertebral bodies, identified using two parallel segmentation approaches, and validates the results through comparison to manually annotated landmarks. The algorithm identified the vertebral corners with an average detection error under five pixels in the C3–C6 vertebrae, with the lowest average error of 1.65 pixels in C4. C7 yielded the largest average error of 6.15 pixels. No significant difference was observed between the intervertebral angles computed using the manually annotated and automatically detected landmarks ($$p>0.05$$). The proposed method does not require large amounts of data for training, eliminates the necessity for manual annotations, and allows for real-time intervertebral motion analysis of the cervical spine.

    KW - Automatic detection

    KW - Cervical spine

    KW - Segmentation

    KW - Vertebral landmarks

    KW - Videofluoroscopy

    UR - http://www.scopus.com/inward/record.url?scp=85066904461&partnerID=8YFLogxK

    U2 - 10.1007/978-3-030-20205-7_18

    DO - 10.1007/978-3-030-20205-7_18

    M3 - Article in proceeding

    SN - 978-3-030-20204-0

    SP - 209

    EP - 220

    BT - Image Analysis

    A2 - Felsberg, Michael

    A2 - Forssén, Per-Erik

    A2 - Unger, Jonas

    A2 - Sintorn, Ida-Maria

    PB - Springer

    ER -

    Jakobsen IMG, Plocharski M. Automatic Detection of Cervical Vertebral Landmarks for Fluoroscopic Joint Motion Analysis. I Felsberg M, Forssén P-E, Unger J, Sintorn I-M, red., Image Analysis : 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings. Springer. 2019. s. 209-220. (Lecture Notes in Computer Science, Bind 11482). https://doi.org/10.1007/978-3-030-20205-7_18