Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS)

T. Araújo, Momen Abayazid, M.J.C.M. Rutten, Sarthak Misra

    Research output: Contribution to journalArticleAcademicpeer-review

    5 Citations (Scopus)

    Abstract

    Background Ultrasound is an effective tool for breast cancer diagnosis. However, its relatively low image quality makes small lesion analysis challenging. This promotes the development of tools to help clinicians in the diagnosis. Methods We propose a method for segmentation and three-dimensional (3D) reconstruction of lesions from ultrasound images acquired using the automated breast volume scanner (ABVS). Segmentation and reconstruction algorithms are applied to obtain the lesion's 3D geometry. A total of 140 artificial lesions with different sizes and shapes are reconstructed in gelatin-based phantoms and biological tissue. Dice similarity coefficient (DSC) is used to evaluate the reconstructed shapes. The algorithm is tested using a human breast phantom and clinical data from six patients. Results DSC values are 0.86 ± 0.06 and 0.86 ± 0.05 for gelatin-based phantoms and biological tissue, respectively. The results are validated by a specialized clinician. Conclusions Evaluation metrics show that the algorithm accurately segments and reconstructs various lesions
    Original languageEnglish
    Article numbere1767
    JournalInternational journal of medical robotics and computer assisted surgery
    Volume13
    Issue number3
    DOIs
    Publication statusPublished - Sep 2017

    Fingerprint

    Breast
    Gelatin
    Tissue
    Image quality
    Ultrasonics
    Breast Neoplasms
    Geometry

    Keywords

    • METIS-319391
    • IR-103367

    Cite this

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    title = "Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS)",
    abstract = "Background Ultrasound is an effective tool for breast cancer diagnosis. However, its relatively low image quality makes small lesion analysis challenging. This promotes the development of tools to help clinicians in the diagnosis. Methods We propose a method for segmentation and three-dimensional (3D) reconstruction of lesions from ultrasound images acquired using the automated breast volume scanner (ABVS). Segmentation and reconstruction algorithms are applied to obtain the lesion's 3D geometry. A total of 140 artificial lesions with different sizes and shapes are reconstructed in gelatin-based phantoms and biological tissue. Dice similarity coefficient (DSC) is used to evaluate the reconstructed shapes. The algorithm is tested using a human breast phantom and clinical data from six patients. Results DSC values are 0.86 ± 0.06 and 0.86 ± 0.05 for gelatin-based phantoms and biological tissue, respectively. The results are validated by a specialized clinician. Conclusions Evaluation metrics show that the algorithm accurately segments and reconstructs various lesions",
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    author = "T. Ara{\'u}jo and Momen Abayazid and M.J.C.M. Rutten and Sarthak Misra",
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    Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS). / Araújo, T.; Abayazid, Momen; Rutten, M.J.C.M.; Misra, Sarthak.

    In: International journal of medical robotics and computer assisted surgery, Vol. 13, No. 3, e1767, 09.2017.

    Research output: Contribution to journalArticleAcademicpeer-review

    TY - JOUR

    T1 - Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS)

    AU - Araújo, T.

    AU - Abayazid, Momen

    AU - Rutten, M.J.C.M.

    AU - Misra, Sarthak

    N1 - Online first

    PY - 2017/9

    Y1 - 2017/9

    N2 - Background Ultrasound is an effective tool for breast cancer diagnosis. However, its relatively low image quality makes small lesion analysis challenging. This promotes the development of tools to help clinicians in the diagnosis. Methods We propose a method for segmentation and three-dimensional (3D) reconstruction of lesions from ultrasound images acquired using the automated breast volume scanner (ABVS). Segmentation and reconstruction algorithms are applied to obtain the lesion's 3D geometry. A total of 140 artificial lesions with different sizes and shapes are reconstructed in gelatin-based phantoms and biological tissue. Dice similarity coefficient (DSC) is used to evaluate the reconstructed shapes. The algorithm is tested using a human breast phantom and clinical data from six patients. Results DSC values are 0.86 ± 0.06 and 0.86 ± 0.05 for gelatin-based phantoms and biological tissue, respectively. The results are validated by a specialized clinician. Conclusions Evaluation metrics show that the algorithm accurately segments and reconstructs various lesions

    AB - Background Ultrasound is an effective tool for breast cancer diagnosis. However, its relatively low image quality makes small lesion analysis challenging. This promotes the development of tools to help clinicians in the diagnosis. Methods We propose a method for segmentation and three-dimensional (3D) reconstruction of lesions from ultrasound images acquired using the automated breast volume scanner (ABVS). Segmentation and reconstruction algorithms are applied to obtain the lesion's 3D geometry. A total of 140 artificial lesions with different sizes and shapes are reconstructed in gelatin-based phantoms and biological tissue. Dice similarity coefficient (DSC) is used to evaluate the reconstructed shapes. The algorithm is tested using a human breast phantom and clinical data from six patients. Results DSC values are 0.86 ± 0.06 and 0.86 ± 0.05 for gelatin-based phantoms and biological tissue, respectively. The results are validated by a specialized clinician. Conclusions Evaluation metrics show that the algorithm accurately segments and reconstructs various lesions

    KW - METIS-319391

    KW - IR-103367

    U2 - 10.1002/rcs.1767

    DO - 10.1002/rcs.1767

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    VL - 13

    JO - International Journal of Medical Robotics and Computer Assisted Surgery

    JF - International Journal of Medical Robotics and Computer Assisted Surgery

    SN - 1478-5951

    IS - 3

    M1 - e1767

    ER -