Performance of novel deep learning network with the incorporation of the automatic segmentation network for diagnosis of breast cancer in automated breast ultrasound

Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249

PubMed  Google Scholar 

Birnbaum JK, Duggan C, Anderson BO, Etzioni R (2018) Early detection and treatment strategies for breast cancer in low-income and upper middle-income countries: a modelling study. Lancet Glob Health 6:e885–e893

PubMed  PubMed Central  Article  Google Scholar 

Li E, Guida JL, Tian Y et al (2019) Associations between mammographic density and tumor characteristics in Chinese women with breast cancer. Breast Cancer Res Treat 177:527–536

CAS  PubMed  PubMed Central  Article  Google Scholar 

Boyd NF, Guo H, Martin LJ et al (2007) Mammographic density and the risk and detection of breast cancer. N Engl J Med 356:227–236

CAS  PubMed  Article  Google Scholar 

Vachon CM, Pankratz VS, Scott CG et al (2007) Longitudinal trends in mammographic percent density and breast cancer risk. Cancer Epidemiol Biomark Prev 16:921–928

Article  Google Scholar 

Jia M, Lin X, Zhou X et al (2020) Diagnostic performance of automated breast ultrasound and handheld ultrasound in women with dense breasts. Breast Cancer Res Treat 181:589–597

PubMed  Article  Google Scholar 

Yun G, Kim SM, Yun B, Ahn HS, Jang M (2019) Reliability of automated versus handheld breast ultrasound examinations of suspicious breast masses. Ultrasonography 38:264–271

PubMed  Article  Google Scholar 

Brem RF, Tabár L, Duffy SW et al (2015) Assessing improvement in detection of breast cancer with three-dimensional automated breast US in women with dense breast tissue: the SomoInsight Study. Radiology 274:663–673

PubMed  Article  Google Scholar 

Wilczek B, Wilczek HE, Rasouliyan L, Leifland K (2016) Adding 3D automated breast ultrasound to mammography screening in women with heterogeneously and extremely dense breasts: report from a hospital-based, high-volume, single-center breast cancer screening program. Eur J Radiol 85:1554–1563

PubMed  Article  Google Scholar 

Rella R, Belli P, Giuliani M et al (2018) Automated Breast Ultrasonography (ABUS) in the screening and diagnostic setting: indications and practical use. Acad Radiol 25:1457–1470

PubMed  Article  Google Scholar 

Zhang L, Bao LY, Tan YJ et al (2019) Diagnostic performance using automated breast ultrasound system for breast cancer in Chinese women aged 40 years or older: a comparative study. Ultrasound Med Biol 45:3137–3144

PubMed  Article  Google Scholar 

Kelly KM, Dean J, Comulada WS, Lee SJ (2010) Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts. Eur Radiol 20:734–742

PubMed  Article  Google Scholar 

van Zelst JCM, Tan T, Clauser P et al (2018) Dedicated computer-aided detection software for automated 3D breast ultrasound; an efficient tool for the radiologist in supplemental screening of women with dense breasts. Eur Radiol 28:2996–3006

PubMed  PubMed Central  Article  Google Scholar 

van Zelst JCM, Tan T, Platel B et al (2017) Improved cancer detection in automated breast ultrasound by radiologists using computer aided detection. Eur J Radiol 89:54–59

PubMed  Article  Google Scholar 

Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ (2017) Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging 30:449–459

PubMed  PubMed Central  Article  Google Scholar 

Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500–510

CAS  PubMed  PubMed Central  Article  Google Scholar 

Chambara N, Ying M (2019) The diagnostic efficiency of ultrasound computer-aided diagnosis in differentiating thyroid nodules: a systematic review and narrative synthesis. Cancers 11:1759

PubMed Central  Article  Google Scholar 

Ha T, Jung Y, Kim JY, Park SY, Kang DK, Kim TH (2019) Comparison of the diagnostic performance of abbreviated MRI and full diagnostic MRI using a computer-aided diagnosis (CAD) system in patients with a personal history of breast cancer: the effect of CAD-generated kinetic features on reader performance. Clin Radiol 74:817.e815–817.e821

Article  Google Scholar 

Wang Y, Wang N, Xu M et al (2020) Deeply-supervised networks with threshold loss for cancer detection in automated breast ultrasound. IEEE Trans Med Imaging 39:866–876

PubMed  Article  Google Scholar 

Li Y, Wu W, Chen H, Cheng L, Wang S (2020) 3D tumor detection in automated breast ultrasound using deep convolutional neural network. Med Phys 47:5669–5680

PubMed  Article  Google Scholar 

Wang Y, Choi EJ, Choi Y, Zhang H, Jin GY, Ko SB (2020) Breast cancer classification in automated breast ultrasound using multiview convolutional neural network with transfer learning. Ultrasound Med Biol 46:1119–1132

PubMed  Article  Google Scholar 

Jiang Y, Inciardi MF, Edwards AV, Papaioannou J (2018) Interpretation time using a concurrent-read computer-aided detection system for automated breast ultrasound in breast cancer screening of women with dense breast tissue. AJR Am J Roentgenol 211:452–461

PubMed  Article  Google Scholar 

Yang S, Gao X, Liu L et al (2019) Performance and reading time of automated breast US with or without computer-aided detection. Radiology 292:540–549

PubMed  Article  Google Scholar 

Moon WK, Huang YS, Hsu CH et al (2020) Computer-aided tumor detection in automated breast ultrasound using a 3-D convolutional neural network. Comput Methods Prog Biomed 190:105360

Article  Google Scholar 

Chiang TC, Huang YS, Chen RT, Huang CS, Chang RF (2019) Tumor detection in automated breast ultrasound using 3-D CNN and prioritized candidate aggregation. IEEE Trans Med Imaging 38:240–249

PubMed  Article  Google Scholar 

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 770-778. https://doi.org/10.1109/CVPR.2016.90

American College of Radiology (2013) ACR BI-RADS® Atlas. Breast Imaging Reporting and Data System, 5th edn. Acadmerican College of Radiology, Reston

Google Scholar 

Zheng FY, Yan LX, Huang BJ et al (2015) Comparison of retraction phenomenon and BI-RADS-US descriptors in differentiating benign and malignant breast masses using an automated breast volume scanner. Eur J Radiol 84:2123–2129

PubMed  Article  Google Scholar 

Kim WH, Moon WK, Kim SJ et al (2013) Ultrasonographic assessment of breast density. Breast Cancer Res Treat 138:851–859

PubMed  Article  Google Scholar 

Noble JA, Boukerroui D (2006) Ultrasound image segmentation: a survey. IEEE Trans Med Imaging 25:987–1010

PubMed  Article  Google Scholar 

Gu P, Lee WM, Roubidoux MA, Yuan J, Wang X, Carson PL (2016) Automated 3D ultrasound image segmentation to aid breast cancer image interpretation. Ultrasonics 65:51–58

CAS  PubMed  Article  Google Scholar 

Liu L, Li K, Qin W et al (2018) Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images. Med Biol Eng Comput 56:183–199

CAS  PubMed  PubMed Central  Article  Google Scholar 

Pan P, Chen H, Li Y, Cai N, Cheng L, Wang S (2021) Tumor segmentation in automated whole breast ultrasound using bidirectional LSTM neural network and attention mechanism. Ultrasonics 110:106271

PubMed  Article  Google Scholar 

Calas MJ, Almeida RM, Gutfilen B, Pereira WC (2010) Intraobserver interpretation of breast ultrasonography following the BI-RADS classification. Eur J Radiol 74:525–528

CAS  PubMed  Article  Google Scholar 

Li XA, Tai A, Arthur DW et al (2009) Variability of target and normal structure delineation for breast cancer radiotherapy: an RTOG multi-institutional and multiobserver study. Int J Radiat Oncol Biol Phys 73:944–951

PubMed  PubMed Central  Article  Google Scholar 

Hu Y, Guo Y, Wang Y et al (2019) Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model. Med Phys 46:215–228

PubMed  Article  Google Scholar 

Lei Y, He X, Yao J et al (2021) Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R-CNN. Med Phys 48:204–214

CAS  PubMed  Article  Google Scholar 

Zhou Y, Chen H, Li Y et al (2021) Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images. Med Image Anal 70:101918

PubMed  Article  Google Scholar 

Wang F, Liu X, Yuan N et al (2020) Study on automatic detection and classification of breast nodule using deep convolutional neural network system. J Thorac Dis 12:4690–4701

PubMed  PubMed Central  Article  Google Scholar 

Xu X, Bao L, Tan Y, Zhu L, Kong F, Wang W (2018) 1000-case reader study of radiologists’ performance in interpretation of automated breast volume scanner images with a computer-aided detection system. Ultrasound Med Biol 44:1694–1702

PubMed  Article  Google Scholar 

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