Advertisement

Abdominal Radiology

, Volume 43, Issue 4, pp 786–799 | Cite as

Machine learning for medical ultrasound: status, methods, and future opportunities

  • Laura J. Brattain
  • Brian A. Telfer
  • Manish Dhyani
  • Joseph R. Grajo
  • Anthony E. Samir
Invited article

Abstract

Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.

Keywords

Deep learning Elastography Machine learning Medical ultrasound Sonography 

Notes

Acknowledgments

This material is based upon work supported by the Assistant Secretary of Defense for Research and Engineering under Air Force Contract No. FA8721-05-C-0002 and/or FA8702-15-D-0001. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Assistant Secretary of Defense for Research and Engineering. This work is also supported by the NIBIB of the National Institutes of Health under award numbers HHSN268201300071 C and K23 EB020710. The authors are solely responsible for the content and the work does not represent the official views of the National Institutes of Health.

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Wang S, Summers RM (2012) Machine learning and radiology. Med Image Anal 16(5):933–951PubMedPubMedCentralCrossRefGoogle Scholar
  2. 2.
    Shen D, Wu G, Suk H-I (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248PubMedPubMedCentralCrossRefGoogle Scholar
  3. 3.
    Litjens G et al. (2017) A survey on deep learning in medical image analysis. ArXiv Prepr. ArXiv170205747Google Scholar
  4. 4.
    Ravi D, et al. (2017) Deep Learning for health informatics. IEEE J Biomed Health Inform 21(1):4–21PubMedCrossRefGoogle Scholar
  5. 5.
    Cassinotto C, et al. (2014) Non-invasive assessment of liver fibrosis with impulse elastography: comparison of Supersonic Shear Imaging with ARFI and FibroScan®. J. Hepatol. 61(3):550–557PubMedCrossRefGoogle Scholar
  6. 6.
    Ferraioli G, Parekh P, Levitov AB, Filice C (2014) Shear wave elastography for evaluation of liver fibrosis. J. Ultrasound Med 33(2):197–203PubMedCrossRefGoogle Scholar
  7. 7.
    Poynard T, et al. (2013) Liver fibrosis evaluation using real-time shear wave elastography: applicability and diagnostic performance using methods without a gold standard. J Hepatol 58(5):928–935PubMedCrossRefGoogle Scholar
  8. 8.
    Samir AE, et al. (2014) Shear-wave elastography for the estimation of liver fibrosis in chronic liver disease: determining accuracy and ideal site for measurement. Radiology 274(3):888–896PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Liu B, et al. (2016) Breast lesions: quantitative diagnosis using ultrasound shear wave elastography—a systematic review and meta-analysis. Ultrasound Med Biol 42(4):835–847PubMedCrossRefGoogle Scholar
  10. 10.
    Wang M, et al. (2017) Differential diagnosis of breast category 3 and 4 nodules through BI-RADS classification in conjunction with shear wave elastography. Ultrasound Med Biol 43(3):601–606PubMedCrossRefGoogle Scholar
  11. 11.
    Wang ZL, Li Y, Wan WB, Li N, Tang J (2017) Shear-wave elastography: could it be helpful for the diagnosis of non-mass-like breast lesions? Ultrasound Med Biol 43(1):83–90PubMedCrossRefGoogle Scholar
  12. 12.
    Anvari A, Dhyani M, Stephen AE, Samir AE (2016) Reliability of shear-wave elastography estimates of the young modulus of tissue in follicular thyroid neoplasms. Am J Roentgenol 206(3):609–616CrossRefGoogle Scholar
  13. 13.
    Dhyani M, Li C, Samir AE, Stephen AE (2017) Elastography: applications and limitations of a new technology. Advanced thyroid and parathyroid ultrasound. New York: Springer, pp 67–73CrossRefGoogle Scholar
  14. 14.
    Ding J, Cheng HD, Huang J, Zhang Y, Liu J (2012) An improved quantitative measurement for thyroid cancer detection based on elastography. Eur J Radiol 81(4):800–805PubMedCrossRefGoogle Scholar
  15. 15.
    Park AY, Son EJ, Han K, et al. (2015) Shear wave elastography of thyroid nodules for the prediction of malignancy in a large scale study. Eur J Radiol 84(3):407–412PubMedCrossRefGoogle Scholar
  16. 16.
    Eby SF, et al. (2015) Shear wave elastography of passive skeletal muscle stiffness: influences of sex and age throughout adulthood. Clin Biomech 30(1):22–27CrossRefGoogle Scholar
  17. 17.
    Pass B, Jafari M, Rowbotham E, et al. (2017) Do quantitative and qualitative shear wave elastography have a role in evaluating musculoskeletal soft tissue masses? Eur Radiol 27(2):723–731PubMedCrossRefGoogle Scholar
  18. 18.
    Taljanovic MS, et al. (2017) Shear-wave elastography: basic physics and musculoskeletal applications. RadioGraphics 37(3):855–870PubMedCrossRefGoogle Scholar
  19. 19.
    Aubry S, Nueffer J-P, Tanter M, et al. (2014) Viscoelasticity in Achilles tendonopathy: quantitative assessment by using real-time shear-wave elastography. Radiology 274(3):821–829PubMedCrossRefGoogle Scholar
  20. 20.
    Zhang ZJ, Ng GY, Lee WC, Fu SN (2014) Changes in morphological and elastic properties of patellar tendon in athletes with unilateral patellar tendinopathy and their relationships with pain and functional disability. PLoS ONE 9(10):e108337PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Rouvière O, et al. (2017) Stiffness of benign and malignant prostate tissue measured by shear-wave elastography: a preliminary study. Eur Radiol 27(5):1858–1866PubMedCrossRefGoogle Scholar
  22. 22.
    Sang L, Wang X, Xu D, Cai Y (2017) Accuracy of shear wave elastography for the diagnosis of prostate cancer: a meta-analysis. Sci Rep 7(1):1949CrossRefGoogle Scholar
  23. 23.
    Woo S, Suh CH, Kim SY, Cho JY, Kim SH (2017) Shear-wave elastography for detection of prostate cancer: a systematic review and diagnostic meta-analysis. Am J Roentgenol 209:1–9CrossRefGoogle Scholar
  24. 24.
    D’Onofrio M, Crosara S, De Robertis R, Canestrini S, Mucelli RP (2015) Contrast-enhanced ultrasound of focal liver lesions. Am J Roentgenol 205(1):W56–W66CrossRefGoogle Scholar
  25. 25.
    Kim TK, Jang H-J (2014) Contrast-enhanced ultrasound in the diagnosis of nodules in liver cirrhosis. World J Gastroenterol 20(13):3590–3596PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Strobel D, et al. (2008) Contrast-enhanced ultrasound for the characterization of focal liver lesions–diagnostic accuracy in clinical practice (DEGUM multicenter trial). Ultraschall Med Stuttg Ger 29(5):499–505CrossRefGoogle Scholar
  27. 27.
    Westwood M, et al. (2013) Contrast-enhanced ultrasound using SonoVue® (sulphur hexafluoride microbubbles) compared with contrast-enhanced computed tomography and contrast-enhanced magnetic resonance imaging for the characterisation of focal liver lesions and detection of liver metastases: a systematic review and cost-effectiveness analysis. Health Technol Assess Winch Engl 17(16):1–243Google Scholar
  28. 28.
    Oh TH, Lee YH, Seo IY (2014) Diagnostic efficacy of contrast-enhanced ultrasound for small renal masses. Korean J Urol 55(9):587–592PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Yuan Z, Quan J, Yunxiao Z, Jian C, Zhu H (2015) Contrast-enhanced ultrasound in the diagnosis of solitary thyroid nodules. J Cancer Res Ther 11(1):41–45PubMedCrossRefGoogle Scholar
  30. 30.
    Li W, et al. (2014) Real-time contrast enhanced ultrasound imaging of focal splenic lesions. Eur J Radiol 83(4):646–653PubMedCrossRefGoogle Scholar
  31. 31.
    Baur ADJ, et al. (2017) A direct comparison of contrast-enhanced ultrasound and dynamic contrast-enhanced magnetic resonance imaging for prostate cancer detection and prediction of aggressiveness. Eur Radiol .  https://doi.org/10.1007/s00330-017-5192-2 PubMedGoogle Scholar
  32. 32.
    Bishop CM (2006) Pattern recognition and machine learning. New York: SpringerGoogle Scholar
  33. 33.
    Mitchell TM (1997) Machine learning. WCB. Boston: McGraw-HillGoogle Scholar
  34. 34.
    De Mantaras RL, Armengol E (1998) Machine learning from examples: inductive and lazy methods. Data Knowl Eng 25(1–2):99–123CrossRefGoogle Scholar
  35. 35.
    Dutton DM, Conroy GV (1997) A review of machine learning. Knowl Eng Rev 12(4):341–367CrossRefGoogle Scholar
  36. 36.
    Kotsiantis SB, Zaharakis ID, Pintelas PE (2006) Machine learning: a review of classification and combining techniques. Artif Intell Rev 26(3):159–190CrossRefGoogle Scholar
  37. 37.
    Torresani L (2014) Weakly supervised learning”. Computer vision. New York: Springer, pp 883–885CrossRefGoogle Scholar
  38. 38.
    Mohri M, Rostamizadeh A, Talwalkar A (2012) Foundations of machine learning. Cambridge: MIT pressGoogle Scholar
  39. 39.
    Soh L-K, Tsatsoulis C (1999) Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens 37(2):780–795CrossRefGoogle Scholar
  40. 40.
    Materka A, Strzelecki M et al. (1998) Texture analysis methods–a review. Technical University of Lodz, Institute of Electronics, COST B11 Report, Brussels, pp 9–11Google Scholar
  41. 41.
    Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52CrossRefGoogle Scholar
  42. 42.
    Liaw A, Wiener M, et al. (2002) Classification and regression by randomForest. R News 2(3):18–22Google Scholar
  43. 43.
    Cortes C, Vapnik V (1995) Support vector machine. Mach Learn 20(3):273–297Google Scholar
  44. 44.
    Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300CrossRefGoogle Scholar
  45. 45.
    White H (1990) Connectionist nonparametric regression: multilayer feedforward networks can learn arbitrary mappings. Neural Netw 3(5):535–549CrossRefGoogle Scholar
  46. 46.
    Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366CrossRefGoogle Scholar
  47. 47.
    Leshno M, Lin VY, Pinkus A, Schocken S (1993) Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw 6(6):861–867CrossRefGoogle Scholar
  48. 48.
    Geisser S (1993) Predictive inference: an introduction. New York: Chapman & HallCrossRefGoogle Scholar
  49. 49.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444PubMedCrossRefGoogle Scholar
  50. 50.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google Scholar
  51. 51.
    Szegedy C et al.(2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9Google Scholar
  52. 52.
    Sermanet P, Kavukcuoglu K, Chintala S, LeCun Y (2013) Pedestrian detection with unsupervised multi-stage feature learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3626–3633Google Scholar
  53. 53.
    Szegedy C, Toshev A, Erhan D (2013) Deep neural networks for object detection. In: Advances in neural information processing systems, pp 2553–2561Google Scholar
  54. 54.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition, ArXiv Prepr. ArXiv14091556Google Scholar
  55. 55.
    Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8(1):98–113PubMedCrossRefGoogle Scholar
  56. 56.
    Matsugu M, Mori K, Mitari Y, Kaneda Y (2003) Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw 16(5):555–559PubMedCrossRefGoogle Scholar
  57. 57.
    Farfade SS, Saberian MJ, Li LJ (2015) Multi-view face detection using deep convolutional neural networks. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp 643–650Google Scholar
  58. 58.
    Turaga SC, et al. (2010) Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput 22(2):511–538PubMedCrossRefGoogle Scholar
  59. 59.
    Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3431–3440Google Scholar
  60. 60.
    Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai 14:1137–1145Google Scholar
  61. 61.
    Arlot S, Celisse A (2010) A survey of cross-validation procedures for model selection. Stat Surv 4:40–79CrossRefGoogle Scholar
  62. 62.
    Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36PubMedCrossRefGoogle Scholar
  63. 63.
    Garra BS, Krasner BH, Horii SC, et al. (1993) Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis. Ultrason Imaging 15(4):267–285PubMedCrossRefGoogle Scholar
  64. 64.
    Maclin PS, Dempsey J (1992) Using an artificial neural network to diagnose hepatic masses. J Med Syst 16(5):215–225PubMedCrossRefGoogle Scholar
  65. 65.
    Giger ML, Karssemeijer N, Schnabel JA (2013) Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu Rev Biomed Eng 15(1):327–357PubMedCrossRefGoogle Scholar
  66. 66.
    Shan J, Alam SK, Garra B, Zhang Y, Ahmed T (2016) Computer-aided diagnosis for breast ultrasound using computerized BI-RADS features and machine learning methods. Ultrasound Med Biol 42(4):980–988PubMedCrossRefGoogle Scholar
  67. 67.
    Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117PubMedCrossRefGoogle Scholar
  68. 68.
    Carbonell JG, Michalski RS, Mitchell TM (1983) An overview of machine learning. Machine learning. New york: Springer, pp 3–23Google Scholar
  69. 69.
    Barinov L, Jairaj A, Paster L et al. (2016) Decision quality support in diagnostic breast ultrasound through artificial Intelligence. In: Signal Processing in Medicine and Biology Symposium, pp 1–4Google Scholar
  70. 70.
    Choi YJ, et al. (2017) A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of thyroid nodules on ultrasound: initial clinical assessment. Thyroid 27(4):546–552PubMedCrossRefGoogle Scholar
  71. 71.
    Hiramatsu Y, Muramatsu C, Kobayashi H, Hara , Fujita H (2017) Automated detection of masses on whole breast volume ultrasound scanner: false positive reduction using deep convolutional neural network. Med Imaging .  https://doi.org/10.1117/12.2254581 Google Scholar
  72. 72.
    Lekadir K, et al. (2017) A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound. IEEE J Biomed Health Inform 21(1):48–55PubMedCrossRefGoogle Scholar
  73. 73.
    Cheng PM, Malhi HS (2017) Transfer learning with convolutional neural networks for classification of abdominal ultrasound images. J Digit Imaging 30(2):234–243PubMedCrossRefGoogle Scholar
  74. 74.
    Antropova N, Huynh BQ, Giger ML (2017) A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys.  https://doi.org/10.1002/mp.12453 PubMedGoogle Scholar
  75. 75.
    Qi H, Collins S, Noble A (2017) Weakly supervised learning of placental ultrasound images with residual networks. In: Annual Conference on Medical Image Understanding and Analysis, pp 98–108Google Scholar
  76. 76.
    Cunningham R, Harding P, Loram I (2017) Deep residual networks for quantification of muscle fiber orientation and curvature from ultrasound images. In: Hernández MV, González-Castro V, González-Castro V (eds) Medical image understanding and analysis, vol. 723. Cham: Springer, pp 63–73CrossRefGoogle Scholar
  77. 77.
    Namburete AI, Stebbing RV, Kemp B, et al. (2015) Learning-based prediction of gestational age from ultrasound images of the fetal brain. Med. Image Anal. 21(1):72–86PubMedPubMedCentralCrossRefGoogle Scholar
  78. 78.
    Cary TW, Reamer CB, Sultan LR, Mohler ER, Sehgal CM (2014) Brachial artery vasomotion and transducer pressure effect on measurements by active contour segmentation on ultrasound: brachial artery vasomotion and transducer pressure effect. Med Phys 41(2):022901PubMedPubMedCentralCrossRefGoogle Scholar
  79. 79.
    Noble JA, Boukerroui D (2006) Ultrasound image segmentation: a survey. IEEE Trans Med Imaging 25(8):987–1010PubMedCrossRefGoogle Scholar
  80. 80.
    Noble JA (2010) Ultrasound image segmentation and tissue characterization. Proc Inst Mech Eng Part H 224(2):307–316CrossRefGoogle Scholar
  81. 81.
    Torbati N, Ayatollahi A, Kermani A (2014) An efficient neural network based method for medical image segmentation. Comput Biol Med 44:76–87PubMedCrossRefGoogle Scholar
  82. 82.
    Yang X, Rossi PJ, Jani AB, et al. (2016) 3D transrectal ultrasound (TRUS) prostate segmentation based on optimal feature learning framework. Med Imaging.  https://doi.org/10.1117/12.2216396 Google Scholar
  83. 83.
    Ghose S, et al. (2013) A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images. Med Image Anal 17(6):587–600PubMedCrossRefGoogle Scholar
  84. 84.
    Sultan LR, Xiong H, Zafar HM, et al. (2015) Vascularity assessment of thyroid nodules by quantitative color doppler ultrasound. Ultrasound Med Biol 41(5):1287–1293PubMedCrossRefGoogle Scholar
  85. 85.
    Chauhan A, Sultan LR, Furth EE, et al. (2016) Diagnostic accuracy of hepatorenal index in the detection and grading of hepatic steatosis: factors affecting the accuracy of HRI. J Clin Ultrasound 44(9):580–586PubMedCrossRefGoogle Scholar
  86. 86.
    Noe MH, et al. (2017) High frequency ultrasound: a novel instrument to quantify granuloma burden in cutaneous sarcoidosis. Sarcoidosis Vasc Diffuse Lung Dis 34(2):136–141Google Scholar
  87. 87.
    Xiong H, Sultan LR, Cary TW et al. (2017) The diagnostic performance of leak-plugging automated segmentation vs. manual tracing of breast lesions on ultrasound images. Ultrasound http://journals.sagepub.com/doi/pdf/10.1177/1742271X17690425#articleCitationDownloadContainer. Accessed 17 Jan 2018
  88. 88.
    Carneiro G, Nascimento JC, Freitas A (2012) The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods. IEEE Trans Image Process 21(3):968–982PubMedCrossRefGoogle Scholar
  89. 89.
    Menchón-Lara RM, Sancho-Gómez JL (2015) Fully automatic segmentation of ultrasound common carotid artery images based on machine learning. Neurocomputing 151(P1):161–167CrossRefGoogle Scholar
  90. 90.
    Zhang Y, Ying MT, Yang L, Ahuja AT, Chen DZ (2016) Coarse-to-fine stacked fully convolutional nets for lymph node segmentation in ultrasound images. In: Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference, pp 443–448Google Scholar
  91. 91.
    Looney P et al. (2017) Automatic 3D ultrasound segmentation of the first trimester placenta using deep learning. In: Biomedical Imaging (ISBI 2017), IEEE 14th International Symposium on, pp 279–282Google Scholar
  92. 92.
    Milletari F, et al. (2017) Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput Vis Image Underst 164:92–102CrossRefGoogle Scholar
  93. 93.
    Chen F, Wu D, Liao H (2016) Registration of CT and ultrasound images of the spine with neural network and orientation code mutual information. In: Zheng G, Liao H, Jannin P, Cattin P, Lee S-L (eds) Medical imaging and augmented reality, vol. 9805. Cham: Springer, pp 292–301CrossRefGoogle Scholar
  94. 94.
    Yang X, Fei B (2012) 3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning. In: Proceedings of SPIE, vol 8316, p 83162OGoogle Scholar
  95. 95.
    Gao Y, Maraci MA, Noble JA (2016) Describing ultrasound video content using deep convolutional neural networks. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp 787–790Google Scholar
  96. 96.
    Baumgartner CF, Kamnitsas K, Matthew J et al.(2016) Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 9901 LNCS, pp 203–211Google Scholar
  97. 97.
    Kumar A et al. (2017) Plane identification in fetal ultrasound images using saliency maps and convolutional neural networks. In: Proceedings of the IEEE International Symposium on Biomedical Imaging, pp 791–794Google Scholar
  98. 98.
    Chen H et al. (2015) Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 9349, pp 507–514Google Scholar
  99. 99.
    Yaqub M, Kelly B, Papageorghiou AT, Noble JA (2015) Guided random forests for identification of key fetal anatomy and image categorization in ultrasound scans. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 687–694Google Scholar
  100. 100.
    Gao X, Li W, Loomes M, Wang L (2017) A fused deep learning architecture for viewpoint classification of echocardiography. Inf Fusion 36:103–113CrossRefGoogle Scholar
  101. 101.
    Sundaresan V, Bridge CP, Ioannou C, Noble JA (2017) Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks. In: Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, pp 671–674Google Scholar
  102. 102.
    Khamis H, Zurakhov G, Azar V, et al. (2017) Automatic apical view classification of echocardiograms using a discriminative learning dictionary. Med Image Anal 36:15–21PubMedCrossRefGoogle Scholar
  103. 103.
    Sigrist RM, Liau J, El Kaffas A, Chammas MC, Willmann JK (2017) Ultrasound elastography: review of techniques and clinical applications. Theranostics 7(5):1303PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Rouze NC, Wang MH, Palmeri ML, Nightingale KR (2012) Parameters affecting the resolution and accuracy of 2-D quantitative shear wave images. IEEE Trans Ultrason Ferroelectr Freq Control 59:1729–1740PubMedPubMedCentralCrossRefGoogle Scholar
  105. 105.
    Pellot-Barakat C, Lefort M, Chami L, et al. (2015) Automatic assessment of shear wave elastography quality and measurement reliability in the liver. Ultrasound Med Biol 41(4):936–943PubMedCrossRefGoogle Scholar
  106. 106.
    Wang J, Guo L, Shi X, et al. (2012) Real-time elastography with a novel quantitative technology for assessment of liver fibrosis in chronic hepatitis B. Eur J Radiol 81(1):e31–e36PubMedCrossRefGoogle Scholar
  107. 107.
    Xiao Y, et al. (2014) Computer-aided diagnosis based on quantitative elastographic features with supersonic shear wave imaging. Ultrasound Med Biol 40(2):275–286PubMedCrossRefGoogle Scholar
  108. 108.
    Bhatia KSS, Lam ACL, Pang SWA, Wang D, Ahuja AT (2016) Feasibility study of texture analysis using ultrasound shear wave elastography to predict malignancy in thyroid nodules. Ultrasound Med Biol 42(7):1671–1680PubMedCrossRefGoogle Scholar
  109. 109.
    Gatos I, et al. (2017) A machine-learning algorithm toward color analysis for chronic liver disease classification, employing ultrasound shear wave elastography. Ultrasound Med Biol 43:1797–1810PubMedCrossRefGoogle Scholar
  110. 110.
    Zhang Q, Xiao Y, Chen S, Wang C, Zheng H (2015) Quantification of elastic heterogeneity using contourlet-based texture analysis in shear-wave elastography for breast tumor classification. Ultrasound Med Biol 41(2):588–600PubMedCrossRefGoogle Scholar
  111. 111.
    Zhang Q, et al. (2016) Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics 72:150–157PubMedCrossRefGoogle Scholar
  112. 112.
    Wu K, Chen X, Ding M (2014) Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound. Opt-Int J Light Electron Opt 125(15):4057–4063CrossRefGoogle Scholar
  113. 113.
    Zeng J, Ustun B, Rudin C (2017) Interpretable classification models for recidivism prediction. J R Stat Soc Ser A 180(3):689–722CrossRefGoogle Scholar
  114. 114.
    Shi J, Zhou S, Liu X, et al. (2016) Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset. Neurocomputing 194:87–94CrossRefGoogle Scholar
  115. 115.
    Singh BK, Verma K, Thoke AS (2016) Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images. Expert Syst Appl 66:114–123CrossRefGoogle Scholar
  116. 116.
    Wu WJ, Lin SW, Moon WK (2015) An artificial immune system-based support vector machine approach for classifying ultrasound breast tumor images. J Digit Imaging 28(5):576–585PubMedPubMedCentralCrossRefGoogle Scholar
  117. 117.
    Shan J, Cheng HD, Wang Y (2012) Completely automated segmentation approach for breast ultrasound images using multiple-domain features. Ultrasound Med Biol 38(2):262–275PubMedCrossRefGoogle Scholar
  118. 118.
    Nascimento CDL, Silva SDS, da Silva TA, et al. (2016) Breast tumor classification in ultrasound images using support vector machines and neural networks. Rev Bras Eng Biomed 32(3):283–292Google Scholar
  119. 119.
    Marcomini KD, Carneiro AAO, Schiabel H (2016) Application of artificial neural network models in segmentation and classification of nodules in breast ultrasound digital images. Int J Biomed Imaging 2016:13CrossRefGoogle Scholar
  120. 120.
    Jamieson AR, Giger ML, Drukker K, et al. (2009) Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and t-SNE: nonlinear dimension reduction and representation in breast CADx. Med Phys 37(1):339–351PubMedCentralCrossRefGoogle Scholar
  121. 121.
    Hwang YN, Lee JH, Kim GY, Jiang YY, Kim SM (2015) Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network. Biomed Mater Eng 26:S1599–S1611PubMedGoogle Scholar
  122. 122.
    Suganya R, Kirubakaran R, Rajaram S (2014) Classification and retrieval of focal and diffuse liver from ultrasound images using machine learning techniques. Cham: Springer, pp 253–261Google Scholar
  123. 123.
    Kalyan K, Jakhia B, Lele RD, Joshi M, Chowdhary A (2014) Artificial neural network application in the diagnosis of disease conditions with liver ultrasound images. Adv Bioinforma.  https://doi.org/10.1155/2014/708279 Google Scholar
  124. 124.
    Brattain LJ, Telfer BA, Liteplo AS, Noble VE (2013) Automated B-line scoring on thoracic sonography. J Ultrasound Med 32(12):2185–2190PubMedCrossRefGoogle Scholar
  125. 125.
    Veeramani SK, Muthusamy E (2016) Detection of abnormalities in ultrasound lung image using multi-level RVM classification. J Matern Fetal Neonatal Med 29(11):1844–1852PubMedGoogle Scholar
  126. 126.
    Konig T, Steffen J, Rak M, et al. (2015) Ultrasound texture-based CAD system for detecting neuromuscular diseases. Int J Comput Assist Radiol Surg 10(9):1493–1503PubMedCrossRefGoogle Scholar
  127. 127.
    Srivastava T, Darras BT, Wu JS, Rutkove SB (2012) Machine learning algorithms to classify spinal muscular atrophy subtypes. Neurology 79(4):358–364PubMedPubMedCentralCrossRefGoogle Scholar
  128. 128.
    Sheet D, et al. (2014) Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound. Med Image Anal 18(1):103–117PubMedCrossRefGoogle Scholar
  129. 129.
    Yu S, Tan KK, Sng BL, Li S, Sia AT (2015) Lumbar ultrasound image feature extraction and classification with support vector machine. Ultrasound Med Biol 41(10):2677–2689PubMedCrossRefGoogle Scholar
  130. 130.
    Pathak H, Kulkarni V (2015) Identification of ovarian mass through ultrasound images using machine learning techniques. In: Research in Computational Intelligence and Communication Networks (ICRCICN), 2015 IEEE International Conference, pp. 137–140Google Scholar
  131. 131.
    Aramendía-Vidaurreta V, Cabeza R, Villanueva A, Navallas J, Alcázar JL (2016) Ultrasound image discrimination between benign and malignant adnexal masses based on a neural network approach. Ultrasound Med Biol 42(3):742–752PubMedCrossRefGoogle Scholar
  132. 132.
    Subramanya MB, Kumar V, Mukherjee S, Saini M (2015) SVM-based CAC system for B-mode kidney ultrasound images. J Digit Imaging 28(4):448–458PubMedCrossRefGoogle Scholar
  133. 133.
    Takagi K, Kondo S, Nakamura K, Takiguchi M (2014) Lesion type classification by applying machine-learning technique to contrast-enhanced ultrasound images. IEICE Trans Inf Syst E97D(11):2947–2954CrossRefGoogle Scholar
  134. 134.
    Caxinha M, et al. (2015) Automatic cataract classification based on ultrasound technique using machine learning: a comparative study. Phys Procedia 70:1221–1224CrossRefGoogle Scholar
  135. 135.
    Sjogren AR, Leo MM, Feldman J, Gwin JT (2016) Image segmentation and machine learning for detection of abdominal free fluid in focused assessment with sonography for trauma examinations: a pilot study. J Ultrasound Med 35(11):2501–2509PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Laura J. Brattain
    • 1
  • Brian A. Telfer
    • 1
  • Manish Dhyani
    • 2
    • 4
  • Joseph R. Grajo
    • 3
  • Anthony E. Samir
    • 4
  1. 1.MIT Lincoln LaboratoryLexingtonUSA
  2. 2.Department of Internal MedicineSteward Carney HospitalBostonUSA
  3. 3.Department of Radiology, Division of Abdominal ImagingUniversity of Florida College of MedicineGainesvilleUSA
  4. 4.Division of Ultrasound, Department of Radiology, Center for Ultrasound Research & TranslationMassachusetts General HospitalBostonUSA

Personalised recommendations