A computer-assisted system for handheld whole-breast ultrasonography

  • Filip ŠroubekEmail author
  • Michal Bartoš
  • Jan Schier
  • Zuzana Bílková
  • Barbara Zitová
  • Jan Vydra
  • Iva Macová
  • Jan Daneš
  • Lukáš Lambert
Original Article



Breast ultrasonography (US) presents an alternative to mammography in young asymptomatic individuals and a complementary examination in screening of women with dense breasts. Handheld US is the standard-of-care, yet when used in whole-breast examination, no effort has been devoted to monitoring breast coverage and missed regions, which is the purpose of this study.


We introduce a computer-aided system assisting radiologists and US technologists in covering the whole breast with minimum alteration to the standard workflow. The proposed system comprises a standard US device, proprietary electromagnetic 3D tracking technology and software that combines US visual and tracking data to estimate a probe trajectory, total time spent in different breast segments, and a map of missed regions. A case study, which involved four radiologists (two junior and two senior) performing whole-breast ultrasound in 75 asymptomatic patients, was conducted to test the importance and relevance of the system.


The mean process time per breast was \(74\pm 22\,{\mathrm {s}}\), with no statistically significant difference between the left and the right sides, and slightly longer examination time of junior radiologists. The process time density shows that central parts of the breast have better coverage compared to the periphery. Within the central part, missed regions of minimum detectable size of \(0.09\,{\mathrm {cm}}^2\) occur in \(8\%\) of examinations, and non-negligible \(1\,{\mathrm {cm}}^2\) regions occur in \(3\%\) of cases.


The results of the case study indicate that missed regions are present in handheld whole-breast US, which renders the proposed system for tracking the probe position during examination a valuable tool for monitoring coverage.


Ultrasound Breast Tracking Coverage Cancer Screening 



We would like to thank J. Kostková and A. Zita for assisting in the clinical study.


This study was supported by the Technological Agency of the Czech Republic (TA04011392) and by the First Faculty of Medicine, Charles University in Prague (Progres Q28/LF1, UNCE 204065).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

This prospective study was performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study. Data were collected by the Department of Radiology at Charles University, Prague.


  1. 1.
    Berg WA, Bandos AI, Mendelson EB, Lehrer D, Jong RA, Pisano ED (2016) Ultrasound as the primary screening test for breast cancer: analysis from ACRIN 6666. J Natl Cancer Inst 108(4):djv367CrossRefGoogle Scholar
  2. 2.
    Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, Jong RA, Hislop G, Chiarelli A, Minkin S, Yaffe MJ (2007) Mammographic density and the risk and detection of breast cancer. N Eng J Med 356(3):227–236CrossRefGoogle Scholar
  3. 3.
    Checka CM, Chun JE, Schnabel FR, Lee J, Toth H (2012) The relationship of mammographic density and age: implications for breast cancer screening. Am J Roentgenol 198(3):W292–W295CrossRefGoogle Scholar
  4. 4.
    Corsetti V, Houssami N, Ghirardi M, Ferrari A, Speziani M, Bellarosa S, Remida G, Gasparotti C, Galligioni E, Ciatto S (2011) Evidence of the effect of adjunct ultrasound screening in women with mammography-negative dense breasts: interval breast cancers at 1year follow-up. Eur J Cancer 47(7):1021–1026CrossRefGoogle Scholar
  5. 5.
    Guo Q, Zhang L, Di Z, Ning C, Dong Z, Li Z, Wang D, Liu C, Zhao M, Tian J (2018) Assessing risk category of breast cancer by ultrasound imaging characteristics. Ultrasound Med Biol 44(4):815–824CrossRefGoogle Scholar
  6. 6.
    Huang CS, Yang YW, Chen RT, Lo CM, Lo C, Cheng CF, Lee CS, Chang RF (2017) Whole-breast ultrasound for breast screening and archiving. Ultrasound Med Biol 43(5):926–933CrossRefGoogle Scholar
  7. 7.
    Janssen N, Eppenga R, Peeters MJV, van Duijnhoven F, Oldenburg H, van der Hage J, Rutgers E, Sonke JJ, Kuhlmann K, Ruers T, Nijkamp J (2018) Real-time wireless tumor tracking during breast conserving surgery. Int J Comput Assist Radiol Surg 13(4, SI):531–539CrossRefGoogle Scholar
  8. 8.
    Jiang WW, Li C, Li AH, Zheng YP (2015) A novel breast ultrasound system for providing coronal images: system development and feasibility study. Ultrasonics 56:427–434CrossRefGoogle Scholar
  9. 9.
    Jiang WW, Li C, Li AH, Zheng YP (2016) Clinical evaluation of a 3-d automatic annotation method for breast ultrasound imaging. Ultrasound Med Biol 42(4):870–881CrossRefGoogle Scholar
  10. 10.
    Kaplan SS (2014) Automated whole breast ultrasound. Radiol Clin N Am 52(3):539–546CrossRefGoogle Scholar
  11. 11.
    Larson ED, Lee WM, Roubidoux MA, Goodsitt MM, Lashbrook C, Zafar F, Kripfgans OD, Thomenius K, Carson PL (2016) Automated breast ultrasound: dual-sided compared with single-sided imaging. Ultrasound Med Biol 42(9):2072–2082CrossRefGoogle Scholar
  12. 12.
    Lasso A, Heffter T, Rankin A, Pinter C, Ungi T, Fichtinger G (2014) PLUS: open-source toolkit for ultrasound-guided intervention systems. IEEE Trans Biomed Eng 61(10):2527–2537CrossRefGoogle Scholar
  13. 13.
    Sadjadi H, Hashtrudi-Zaad K, Fichtinger G (2016) Simultaneous electromagnetic tracking and calibration for dynamic field distortion compensation. IEEE Trans Biomed Eng 63(8):1771–1781CrossRefGoogle Scholar
  14. 14.
    Shin HJ, Kim HH, Cha JH (2015) Current status of automated breast ultrasonography. Ultrasonography 34(3):165–172CrossRefGoogle Scholar
  15. 15.
    Thigpen D, Kappler A, Brem R (2018) The role of ultrasound in screening dense breasts: a review of the literature and practical solutions for implementation. Diagnostics 8(1):20CrossRefGoogle Scholar
  16. 16.
    Vourtsis A, Kachulis A (2018) The performance of 3D ABUS versus HHUS in the visualisation and BI-RADS characterisation of breast lesions in a large cohort of 1,886 women. Eur Radiol 28(2):592–601CrossRefGoogle Scholar
  17. 17.
    Watanabe R, Ando T, Osawa M, Ido M, Kousaka J, Mouri Y, Fujii K, Nakano S, Kimura J, Ishiguchi T, Yoshida M, Imai T, Fukutomi T (2017) Second-look us using real-time virtual sonography, a coordinated breast us and mri system with electromagnetic tracking technology: a pilot study. Ultrasound Med Biol 43(10):2362–2371CrossRefGoogle Scholar
  18. 18.
    Watanabe T, Yamaguchi T, Tsunoda H, Kaoku S, Tohno E, Yasuda H, Ban K, Hirokaga K, Tanaka K, Umemoto T, Okuno T, Fujimoto Y, Nakatani S, Ito J, Ueno E (2017) Ultrasound image classification of ductal carcinoma in situ (DCIS) of the breast: analysis of 705 DCIS lesions. Ultrasound Med Biol 43(5):918–925CrossRefGoogle Scholar
  19. 19.
    Zettinig O, Frisch B, Virga S, Esposito M, Rienmueller A, Meyer B, Hennersperger C, Ryang YM, Navab N (2017) 3D ultrasound registration-based visual servoing for neurosurgical navigation. Int J Comput Assist Radiol Surg 12(9):1607–1619CrossRefGoogle Scholar

Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Institute of Information Theory and AutomationCzech Academy of SciencesPrague 8Czech Republic
  2. 2.Medico LtdPragueCzech Republic
  3. 3.Department of Radiology, First Faculty of MedicineCharles University and General University Hospital in PraguePrague 2Czech Republic

Personalised recommendations