Deep learning based classification of breast tumors with shear-wave elastography

Ultrasonics

Volumn 72, Issue , 2016, Pages 150-157

  Zhang, Qi ^a   Xiao, Yang ^b   Dai, Wei ^a   Suo, Jingfeng ^a   Wang, Congzhi ^b   Shi, Jun ^a   Zheng, Hairong ^b  

^a SHANGHAI UNIVERSITY   (China)

^b INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

Breast tumors; Computer aided diagnosis; Deep learning; Point wise gated Boltzmann machine; Shear wave elastography

Indexed keywords

COMPUTER AIDED DIAGNOSIS; COMPUTER AIDED INSTRUCTION; DIAGNOSIS; EXTRACTION; FEATURE EXTRACTION; RECONFIGURABLE HARDWARE; SHEAR FLOW; SHEAR WAVES; SHEARING MACHINES; TUMORS;

BREAST TUMOR; CLASSIFICATION PERFORMANCE; DEEP LEARNING; EXPERIMENTAL EVALUATION; GATED BOLTZMANN MACHINES; RECEIVER OPERATING CHARACTERISTIC CURVES; RESTRICTED BOLTZMANN MACHINE; SHEAR WAVE ELASTOGRAPHY;

MEDICAL IMAGING;

ADULT; ARTIFICIAL NEURAL NETWORK; BIOPSY; BREAST TUMOR; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; DIFFERENTIAL DIAGNOSIS; ECHOMAMMOGRAPHY; ELASTOGRAPHY; FEMALE; HUMAN; PATHOLOGY; PROCEDURES; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY;

ADULT; BIOPSY; BREAST NEOPLASMS; DIAGNOSIS, DIFFERENTIAL; ELASTICITY IMAGING TECHNIQUES; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); RETROSPECTIVE STUDIES; SENSITIVITY AND SPECIFICITY; ULTRASONOGRAPHY, MAMMARY;

EID: 84982182244     PISSN: 0041624X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ultras.2016.08.004     Document Type: Article

References (47)

1. [1] DeSantis, C., Siegel, R., Jemal, A., Breast cancer facts and figures 2013–2014. Am. Cancer Soc., 2013, 1–38.
2. [2] Barr, R.G., Nakashima, K., Amy, D., Cosgrove, D., Farrokh, A., Schafer, F., Bamber, J.C., Castera, L., Choi, B.I., Chou, Y.-H., Dietrich, C.F., Ding, H., Ferraioli, G., Filice, C., Friedrich-Rust, M., Hall, T.J., Nightingale, K.R., Palmeri, M.L., Shiina, T., Suzuki, S., Sporea, I., Wilson, S., Kudo, M., WFUMB guidelines and recommendations for clinical use of ultrasound elastography: Part 2: breast. Ultrasound. Med. Biol. 41 (2015), 1148–1160.
3. [3] Berg, W.A., Mendelson, E.B., Cosgrove, D.O., Doré, C.J., Gay, J., Henry, J.-P., Cohen-Bacrie, C., Quantitative maximum shear-wave stiffness of breast masses as a predictor of histopathologic severity. Am. J. Roentgenol. 205 (2015), 448–455.
4. [4] Berg, W.A., Cosgrove, D.O., Dore, C.J., Schafer, F.K.W., Svensson, W.E., Hooley, R.J., Ohlinger, R., Mendelson, E.B., Balu-Maestro, C., Locatelli, M., Tourasse, C., Cavanaugh, B.C., Juhan, V., Stavros, A.T., Tardivon, A., Gay, J., Henry, J.P., Cohen-Bacrie, C., Investigators, B., Shear-wave elastography improves the specificity of breast US: the BE1 multinational study of 939 masses. Radiology 262 (2012), 435–449.
5. [5] Schafer, F.K.W., Hooley, R.J., Ohlinger, R., Hahne, U., Madjar, H., Svensson, W.E., Balu-Maestro, C., Juhan, V., Athanasiou, A., Mundinger, A., Order, B., Locatelli, M., Cosgrove, D., Wolf, O.J., Henry, J.P., Moutfi, M., Gay, J.M., Cohen-Bacrie, C., ShearWave (TM) elastography BE1 multinational breast study: additional SWE (TM) features support potential to downgrade BI-RADS (R)-3 lesions. Ultraschall Med. 34 (2013), 254–259.
6. [6] Xiao, Y., Zeng, J., Niu, L., Zeng, Q., Wu, T., Wang, C., Zheng, R., Zheng, H., Computer-aided diagnosis based on quantitative elastographic features with supersonic shear wave imaging. Ultrasound. Med. Biol. 40 (2014), 275–286.
7. [7] Zhang, Q., Xiao, Y., Chen, S., Wang, C., Zheng, H., Quantification of elastic heterogeneity using contourlet-based texture analysis in shear-wave elastography for breast tumor classification. Ultrasound. Med. Biol. 41 (2015), 588–600.
8. [8] Wang, Z.L., Sun, L., Li, Y., Li, N., Relationship between elasticity and collagen fiber content in breast disease: a preliminary report. Ultrasonics 57 (2015), 44–49.
9. [9] Maraci, M., Napolitano, R., Papageorghiou, A., Noble, J., Fisher vector encoding for detecting objects of interest in ultrasound videos. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015, IEEE, 651–654.
10. [10] Maraci, M.A., Napolitano, R., Papageorghiou, A., Noble, J.A., Object classification in an ultrasound video using LP-SIFT features. Medical Computer Vision: Algorithms for Big Data, 2014, Springer, 71–81.
11. [11] Moon, W., Lo, C.-M., Chang, J., Huang, C.-S., Chen, J.-H., Chang, R.-F., Quantitative ultrasound analysis for classification of BI-RADS category 3 breast masses. J. Digit. Imag. 26 (2013), 1091–1098.
12. [12] Moon, W.K., Lo, C.M., Chang, J.M., Huang, C.S., Chen, J.H., Chang, R.F., Computer-aided classification of breast masses using speckle features of automated breast ultrasound images. Med. Phys. 39 (2012), 6465–6473.
13. [13] Moon, W.K., Shen, Y.W., Huang, C.S., Chiang, L.R., Chang, R.F., Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images. Ultrasound. Med. Biol. 37 (2011), 539–548.
14. [14] Chang, R.F., Hou, Y.L., Lo, C.M., Huang, C.S., Chen, J.H., Kim, W.H., Chang, J.M., Bae, M.S., Moon, W.K., Quantitative analysis of breast echotexture patterns in automated breast ultrasound images. Med. Phys., 42, 2015, 4566.
15. [15] Cai, L., Wang, X., Wang, Y., Guo, Y., Yu, J., Wang, Y., Robust phase-based texture descriptor for classification of breast ultrasound images. Biomed. Eng. Online, 14, 2015, 26.
16. [16] Tan, T., Platel, B., Huisman, H., Sanchez, C.I., Mus, R., Karssemeijer, N., Computer-aided lesion diagnosis in automated 3-D breast ultrasound using coronal spiculation. IEEE Trans. Med. Imag. 31 (2012), 1034–1042.
17. [17] Tan, T., Platel, B., Twellmann, T., van Schie, G., Mus, R., Grivegnee, A., Mann, R.M., Karssemeijer, N., Evaluation of the effect of computer-aided classification of benign and malignant lesions on reader performance in automated three-dimensional breast ultrasound. Acad. Radiol. 20 (2013), 1381–1388.
18. [18] LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature 521 (2015), 436–444.
19. [19] Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Mastering the game of Go with deep neural networks and tree search. Nature 529 (2016), 484–489.
20. [20] Hinton, G., Where do features come from?. Cogn. Sci. 38 (2014), 1078–1101.
21. [21] Nair, V., Hinton, G.E., Implicit mixtures of restricted Boltzmann machines. Advances in neural information processing systems, 2009, 1145–1152.
22. [22] Sohn, K., Zhou, G., Lee, C., Lee, H., Learning and selecting features jointly with point-wise gated Boltzmann machines. Proceedings of the 30th International Conference on Machine Learning, 2013, 217–225.
23. [23] Hinton, G.E., Osindero, S., Teh, Y.W., A fast learning algorithm for deep belief nets. Neural. Comput. 18 (2006), 1527–1554.
24. [24] Hinton, G.E., Salakhutdinov, R.R., Reducing the dimensionality of data with neural networks. Science 313 (2006), 504–507.
25. [25] Bengio, Y., Courville, A., Vincent, P., Representation learning: a review and new perspectives. IEEE. Trans. Pattern. Anal. 35 (2013), 1798–1828.
26. [26] Larochelle, H., Bengio, Y., Classification using discriminative restricted Boltzmann machines. Proceedings of the 25th International Conference on Machine learning, 2008, ACM, 536–543.
27. [27] Chang, C.-C., Lin, C.-J., LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2 (2011), 1–27.
28. [28] Platt, J., Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classifiers 10 (1999), 61–74.
29. [29] Uniyal, N., Eskandari, H., Abolmaesumi, P., Sojoudi, S., Gordon, P., Warren, L., Rohling, R.N., Salcudean, S.E., Moradi, M., Ultrasound RF time series for classification of breast lesions. IEEE Trans. Med. Imag. 34 (2015), 652–661.
30. [30] Zhang, Q., Li, C., Han, H., Yang, L., Wang, Y., Wang, W., Computer-aided quantification of contrast agent spatial distribution within atherosclerotic plaque in contrast-enhanced ultrasound image sequences. Biomed. Signal Process. Control 13 (2014), 50–61.
31. [31] Erdogan, S.Z., Bilgin, T.T., A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data. IET Commun. 6 (2012), 3281–3287.
32. [32] Huang, G.B., Zhu, Q.Y., Siew, C.K., Extreme learning machine: theory and applications. Neurocomputing 70 (2006), 489–501.
33. [33] Zhou, J., Zhan, W., Chang, C., Zhang, X., Jia, Y., Dong, Y., Zhou, C., Sun, J., Grant, E.G., Breast lesions: evaluation with shear wave elastography, with special emphasis on the “Stiff Rim” sign. Radiology 272 (2014), 63–72.
34. [34] Evans, A., Whelehan, P., Thomson, K., McLean, D., Brauer, K., Purdie, C., Jordan, L., Baker, L., Thompson, A., Quantitative shear wave ultrasound elastography: initial experience in solid breast masses. Breast. Cancer Res., 12, 2010, R104.
35. [35] Tozaki, M., Fukuma, E., Pattern classification of ShearWaveTM elastography images for differential diagnosis between benign and malignant solid breast masses. Acta Radiol. 52 (2011), 1069–1075.
36. [36] Campbell, M.J., Julious, S.A., Altman, D.G., Estimating sample sizes for binary, ordered categorical, and continuous outcomes in two group comparisons. BMJ 311 (1995), 1145–1148.
37. [37] Chang, J.M., Park, I.A., Lee, S.H., Kim, W.H., Bae, M.S., Koo, H.R., Yi, A., Kim, S.J., Cho, N., Moon, W.K., Stiffness of tumours measured by shear-wave elastography correlated with subtypes of breast cancer. Eur. Radiol. 23 (2013), 2450–2458.
38. [38] Zheng, J., Guo, H.Y., Zeng, J., Huang, Z.P., Zheng, B.W., Ren, J., Xu, E.J., Li, K., Zheng, R.Q., Two-dimensional shear-wave elastography and conventional US: the optimal evaluation of liver fibrosis and cirrhosis. Radiology 275 (2015), 290–300.
39. [39] Ahmad, S., Cao, R., Varghese, T., Bidaut, L., Nabi, G., Transrectal quantitative shear wave elastography in the detection and characterisation of prostate cancer. Surg. Endosc. Other Inter. Tech. 27 (2013), 3280–3287.
40. [40] Park, A.Y., Son, E.J., Han, K., Youk, J.H., Kim, J.A., Park, C.S., Shear wave elastography of thyroid nodules for the prediction of malignancy in a large scale study. Eur. J. Radiol. 84 (2015), 407–412.
41. [41] Zhang, Z.J., Ng, G.Y.F., Lee, W.C., Fu, S.N., 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, 2014.
42. [42] Xiao, G.F., Brady, M., Noble, J.A., Zhang, Y.Y., Segmentation of ultrasound B-mode images with intensity inhomogeneity correction. IEEE Trans. Med. Imag. 21 (2002), 48–57.
43. [43] Pons, G., Marti, J., Marti, R., Ganau, S., Noble, J.A., Breast-lesion segmentation combining B-mode and elastography ultrasound. Ultrason. Imag. 38 (2016), 209–224.
44. [44] Shin, H.-C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M., Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imag. 35 (2016), 1285–1298.
45. [45] Loizou, C., Petroudi, S., Pantziaris, M., Nicolaides, A., Pattichis, C., An integrated system for the segmentation of atherosclerotic carotid plaque ultrasound video. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 61 (2014), 86–101.
46. [46] Wang, W., Arora, R., Livescu, K., Bilmes, J., On deep multi-view representation learning. Proceedings of the 32th International Conference on Machine learning, 2015, ACM.
47. [47] Roth, H., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., Summers, R., Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans. Med. Imag. 35 (2016), 1170–1181.