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Real-time automatic tumor segmentation for ultrasound-guided breast-conserving surgery navigation

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Ultrasound-based navigation is a promising method in breast-conserving surgery, but tumor contouring often requires a radiologist at the time of surgery. Our goal is to develop a real-time automatic neural network-based tumor contouring process for intraoperative guidance. Segmentation accuracy is evaluated by both pixel-based metrics and expert visual rating.

Methods

This retrospective study includes 7318 intraoperative ultrasound images acquired from 33 breast cancer patients, randomly split between 80:20 for training and testing. We implement a u-net architecture to label each pixel on ultrasound images as either tumor or healthy breast tissue. Quantitative metrics are calculated to evaluate the model’s accuracy. Contour quality and usability are also assessed by fellowship-trained breast radiologists and surgical oncologists. Additionally, the viability of using our u-net model in an existing surgical navigation system is evaluated by measuring the segmentation frame rate.

Results

The mean dice similarity coefficient of our u-net model is 0.78, with an area under the receiver-operating characteristics curve of 0.94, sensitivity of 0.95, and specificity of 0.67. Expert visual ratings are positive, with 93% of responses rating tumor contour quality at or above 7/10, and 75% of responses rating contour quality at or above 8/10. Real-time tumor segmentation achieved a frame rate of 16 frames-per-second, sufficient for clinical use.

Conclusion

Neural networks trained with intraoperative ultrasound images provide consistent tumor segmentations that are well received by clinicians. These findings suggest that neural networks are a promising adjunct to alleviate radiologist workload as well as improving efficiency in breast-conserving surgery navigation systems.

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References

  1. Alkabban FM, Ferguson T. Breast Cancer. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2021 [cited 2021 Dec 24]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK482286/

  2. Walters S, Maringe C, Butler J, Rachet B, Barrett-Lee P, Bergh J, Boyages J, Christiansen P, Lee M, Warnberg F, Allemani C, Engholm G, Fornander T, Gjertstorff ML, Johannesen TB, Lawrence G, McGahan CE, Middleton R, Steward J, Tracey E, Turner D, Richards MA, Coleman MP (2013) Breast cancer survival and stage at diagnosis in Australia, Canada, Denmark, Norway, Sweden and the UK, 2000–2007: a population-based study. Br J Cancer 108(5):1195–1208

    Article  CAS  Google Scholar 

  3. Chen K, Li S, Li Q, Zhu L, Liu Y, Song E, Fengxi Su (2016) Breast-conserving surgery rates in breast cancer patients with different molecular subtypes: an observational study based on surveillance, epidemiology, and end results (SEER) database. Medicine 95(8):e2593

    Article  Google Scholar 

  4. Maloney BW, McClatchy DM, Pogue BW, Paulsen KD, Wells WA, Barth RJ (2018) Review of methods for intraoperative margin detection for breast conserving surgery. JBO 23(10):100901

    PubMed Central  Google Scholar 

  5. Wu W, Su Z, Ma L, Chang J, Cui J (2020) Process analysis and application summary of surgical navigation system. J Complexity Health Sci 3(1):52–61

    Article  Google Scholar 

  6. Janssen N, Eppenga R, VranckenPeeters M-J, Duijnhoven F, Oldenburg H, van der Hage J, Rutgers E, Sonke J, Kuhlmann K, Ruers T, Nijkamp J (2017) Real-time wireless tumor tracking during breast conserving surgery. Int J Comput Assist Radiol Surg 13:13

    Google Scholar 

  7. Gauvin G, Yeo CT, Ungi T, Merchant S, Lasso A, Jabs D, Vaughan T, Rudan JF, Walker R, Fichtinger G, Engel CJ (2020) Real-time electromagnetic navigation for breast-conserving surgery using NaviKnife technology: a matched case-control study. Breast J 26(3):399–405

    Article  Google Scholar 

  8. Pan H, Wu N, Ding H, Ding Q, Dai J, Ling L, Chen L, Zha X, Liu X, Zhou W, Wang S (2013) Intraoperative ultrasound guidance is associated with clear lumpectomy margins for breast cancer: a systematic review and meta-analysis. PLoS ONE 8(9):e74028

    Article  CAS  Google Scholar 

  9. Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical image computing and computer-assisted intervention – MICCAI 2015. Springer International Publishing, Cham, pp 234–241

    Chapter  Google Scholar 

  10. Zhuang Z, Li N, Joseph Raj AN, Mahesh VGV, Qiu S (2019) An RDAU-NET model for lesion segmentation in breast ultrasound images. PLoS ONE 14(8):e0221535

    Article  CAS  Google Scholar 

  11. Byra M, Jarosik P, Szubert A, Galperin M, Ojeda-Fournier H, Olson L, O’Boyle M, Comstock C, Andre M (2020) Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network. Biomed Signal Process Control 61:102027

    Article  Google Scholar 

  12. Negi A, Raj ANJ, Nersisson R, Zhuang Z, Murugappan M (2020) RDA-UNET-WGAN: an accurate breast ultrasound lesion segmentation using wasserstein generative adversarial networks. Arab J Sci Eng 45(8):6399–6410

    Article  Google Scholar 

  13. Wang Y, Qin C, Lin C, Lin D, Xu M, Luo X, Wang T, Li A, Ni D (2020) 3D Inception U-net with asymmetric loss for cancer detection in automated breast ultrasound. Med Phys 47(11):5582–5591

    Article  Google Scholar 

  14. Amiri M, Brooks R, Behboodi B, Rivaz H (2020) Two-stage ultrasound image segmentation using U-Net and test time augmentation. Int J CARS 15(6):981–988

    Article  Google Scholar 

  15. Punn NS, Agarwal S (2021) RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging. arXiv:210802508

  16. Guo Y, Duan X, Wang C, Guo H (2021) Segmentation and recognition of breast ultrasound images based on an expanded U-Net. PLoS ONE 16(6):e0253202

    Article  CAS  Google Scholar 

  17. Yap MH, Goyal M, Osman FM, Martí R, Denton E, Juette A, Zwiggelaar R (2018) Breast ultrasound lesions recognition: end-to-end deep learning approaches. JMI 6(1):011007

    PubMed  PubMed Central  Google Scholar 

  18. Liao WX, He P, Hao J, Wang XY, Yang RL, An D, Cui LG (2019) Automatic identification of breast ultrasound image based on supervised block-based region segmentation algorithm and features combination migration deep learning model. IEEE J Biomed Health Inform 24(4):984–993

    Article  Google Scholar 

  19. Lazo JF, Moccia S, Frontoni E, De Momi E (2020) Comparison of different CNNs for breast tumor classification from ultrasound images. arXiv:201214517. p 1–6

  20. Kim J, Kim HJ, Kim C, Lee JH, Kim KW, Park YM et al (2021) Weakly-supervised deep learning for ultrasound diagnosis of breast cancer. Sci Rep 11(1):24382

    Article  CAS  Google Scholar 

  21. 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):2527–2537

    Article  Google Scholar 

  22. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R (2012) 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30(9):1323–1341

    Article  Google Scholar 

  23. Ungi T, Lasso A, Fichtinger G (2016) Open-source platforms for navigated image-guided interventions. Med Image Anal 33:181–186

    Article  Google Scholar 

  24. Trevethan R (2017) Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice. Front Public Health 5:307

    Article  Google Scholar 

  25. Qualtrics. Qualtrics, https://www.qualtrics.com/ (2021, accessed December 1 2020)

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Funding

Queen’s University, Department of Pathology and Molecular Medicine, Dean’s Doctoral Award (PVNF). Gabor Fichtinger was supported as Canada Research Chair of the Natural Sciences and Engineering Research Council of Canada.

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Authors and Affiliations

  1. School of Medicine, Queen’s University, 88 Stuart Street, Kingston, ON, K7L 3N6, Canada

    Zoe Hu

  2. Department of Pathology and Molecular Medicine, Queen’s University, Kingston, ON, Canada

    Paola V. Nasute Fauerbach

  3. School of Computing, Queen’s University, Kingston, ON, Canada

    Chris Yeung, Tamas Ungi, Parvin Mousavi & Gabor Fichtinger

  4. Department of Surgery, Queen’s University, Kingston, ON, Canada

    John Rudan & Cecil Jay Engel

  5. Department of Radiology, Queen’s University, Kingston, ON, Canada

    Doris Jabs

Authors
  1. Zoe Hu
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  2. Paola V. Nasute Fauerbach
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  3. Chris Yeung
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  4. Tamas Ungi
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  5. John Rudan
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  6. Cecil Jay Engel
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  7. Parvin Mousavi
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  8. Gabor Fichtinger
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  9. Doris Jabs
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Corresponding author

Correspondence to Zoe Hu.

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The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in this study.

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Hu, Z., Nasute Fauerbach, P.V., Yeung, C. et al. Real-time automatic tumor segmentation for ultrasound-guided breast-conserving surgery navigation. Int J CARS 17, 1663–1672 (2022). https://doi.org/10.1007/s11548-022-02658-4

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  • DOI: https://doi.org/10.1007/s11548-022-02658-4

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