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

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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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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

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KW - IR-103367

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JO - International Journal of Medical Robotics and Computer Assisted Surgery

JF - International Journal of Medical Robotics and Computer Assisted Surgery

SN - 1478-5951

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