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Artificial Intelligence for Fetal Ultrasound

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Deep Learning and Medical Applications

Part of the book series: Mathematics in Industry ((MATHINDUSTRY,volume 40))

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Abstract

Diagnostic ultrasound is the most commonly used imaging method in the field of obstetrics and gynecology to estimate various biometrics related to fetal development, fetal well-being, and perinatal prognosis. Until now, ultrasound measurements of fetal health parameters (i.e., amniotic fluid volume, biparietal diameter, head circumference, abdominal circumference, and others) have been made through a cumbersome and time-consuming manual process, and their accuracy depends heavily on the operator’s skill and experience. Therefore, there has been a high demand for an easy-to-use interface for collecting biometrics from fetal ultrasound images to improve clinician workflow efficiency. Traditional methods have fundamental limitations in automating biometric measurements from noisy ultrasound images that are often degraded by signal dropouts, reverberation artifacts, missing boundaries, attenuation, shadows, speckles, and so on. Medical imaging is experiencing a paradigm shift due to the remarkable and rapid advancement of deep learning technology, and ultrasound companies, including Samsung Medison, are making every effort to develop a new AI-based system for automated fetal ultrasound diagnosis. The reason for these efforts of ultrasound companies is that AI technology is expected to become a turning point in diagnostic ultrasound. This chapter focuses on fetal ultrasound, explains deep learning-based medical imaging technology, and hopes to help readers discover new possibilities and to provide future directions.

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References

  1. Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2189–2202 (2012)

    Article  Google Scholar 

  2. Arisoy, R., Yayla, M.: Transvaginal sonographic evaluation of the cervix in asymptomatic singleton pregnancy and management options in short cervix. J. Pregnancy (2012)

    Google Scholar 

  3. Baumgartner, C.F., Kamnitsas, K., Matthew, J., Fletcher, T.P., Smith, S., Koch, L.M., Kainz, B., Rueckert, D.: Sononet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans. Med. Imaging 36(11), 2204–2215 (2017)

    Google Scholar 

  4. Beck, S., Wojdyla, D., Say, L., Betran, A.P., Merialdi, M., Requejo, J.H., Rubens, C., Menon, R., Van Look, P.F.: The worldwide incidence of preterm birth: a systematic review of maternal mortality and morbidity. Bull. World Health Organ. 88, 31–38 (2010)

    Google Scholar 

  5. Bennett, N., Burridge, R., Saito, N.: A method to detect and characterize ellipses using the Hough transform. IEEE Trans. Pattern Anal. Mach. Intell. 21(7), 652–657 (1999)

    Article  Google Scholar 

  6. Berghella, V., Palacio, M., Ness, A., Alfirevic, Z., Nicolaides, K.H., Saccone, G.: Cervical length screening for prevention of preterm birth in singleton pregnancy with threatened preterm labor: systematic review and meta-analysis of randomized controlled trials using individual patient-level data. Ultrasound Obstet. Gynecol. 49(3), 322–329 (2017)

    Article  Google Scholar 

  7. Blencowe, H., Cousens, S., Oestergaard, M.Z., Chou, D., Moller, A.B., Narwal, R., Adler, A., Garcia, C.V., Rohde, S., Say, L., et al.: National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. Lancet 379(9832), 2162–2172 (2012)

    Google Scholar 

  8. Cai, Y., Droste, R., Sharma, H., Chatelain, P., Drukker, L., Papageorghiou, A.T. and Noble, J.A.: Spatio-temporal visual attention modelling of standard biometry plane-finding navigation. Med. Image Anal. 65, 101762 (2020)

    Google Scholar 

  9. Campbell, S., Wilkin, D.: Ultrasonic measurement of fetal abdomen circumference in the estimation of fetal weight. BJOG: Int. J. Obstet. Gynaecol. 82(9), 689–697 (1975)

    Google Scholar 

  10. Carvalho, M.H.B., Bittar, R.E., Brizot, M.L., Maganha, P.P.S., Borges da Fonseca, E.S.V., Zugaib, M.: Cervical length at 11–14 weeks’ and 22–24 weeks’ gestation evaluated by transvaginal sonography, and gestational age at delivery. Ultrasound Obstet. Gynecol.: Off. J. Int. Soc. Ultrasound Obstet. Gynecol. 21(2), 135–139 (2003)

    Google Scholar 

  11. Chalana, V., Winter III, T.C., Cyr, D.R., Haynor, D.R., Kim, Y.: Automatic fetal head measurements from sonographic images. Acad. Radiol. 3(8), 628–635 (1996)

    Google Scholar 

  12. Chen, H., Ni, D., Qin, J., Li, S., Yang, X., Wang, T., Heng, P.A.: Standard plane localization in fetal ultrasound via domain transferred deep neural networks. IEEE J. Biomed. Health Inform. 19(5), 1627–1636 (2015)

    Article  Google Scholar 

  13. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: transformers make strong encoders for medical image segmentation (2021). arXiv:2102.04306

  14. Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous convolution for semantic image segmentation (2017). arXiv:1706.05587

  15. Cho, H.C., Sun, S., Hyun, C.M., Kwon, J.Y., Kim, B., Park, Y., Seo, J.K.: Automated ultrasound assessment of amniotic fluid index using deep learning. Med. Image Anal. 69, 101951 (2021)

    Google Scholar 

  16. Coombe-Patterson, J.: Amniotic fluid assessment: amniotic fluid index versus maximum vertical pocket. J. Diagn. Med. Sonogr. 33(4), 280–283 (2017)

    Article  Google Scholar 

  17. Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation. In: Deep Learning and Data Labeling for Medical Applications, pp. 179–187. Springer, Berlin (2016)

    Google Scholar 

  18. Dubil, E.A., Magann, E.F.: Amniotic fluid as a vital sign for fetal wellbeing. Australas. J. Ultrasound Med. 16(2), 62–70 (2013)

    Google Scholar 

  19. Espinoza, J., Good, S., Russell, E., Lee, W.: Does the use of automated fetal biometry improve clinical work flow efficiency? J. Ultrasound Med. 32(5), 847–850 (2013)

    Article  Google Scholar 

  20. Feldman, M.K., Katyal, S., Blackwood, M.S.: US artifacts. Radiographics 29(4), 1179–1189 (2009)

    Google Scholar 

  21. Foi, A., Maggioni, M., Pepe, A., Rueda, S., Noble, J.A., Papageorghiou, A.T., Tohka, J.: Difference of gaussians revolved along elliptical paths for ultrasound fetal head segmentation. Comput. Med. Imaging Graph. 38(8), 774–784 (2014)

    Google Scholar 

  22. Huazhu, F., Cheng, J., Yanwu, X., Wong, D.W.K., Liu, J., Cao, X.: Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018)

    Article  Google Scholar 

  23. Kunihiko, F., Sei, M.: Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. In: Competition and Cooperation in Neural Nets, pp. 267–285. Springer, Berlin (1982)

    Google Scholar 

  24. Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  25. Hadlock, F.P., Harrist, R.B., Sharman, R.S., Deter, R.L., Park, S.K.: Estimation of fetal weight with the use of head, body, and femur measurements-a prospective study. Am. J. Obstet. Gynecol. 151(3), 333–337 (1985)

    Google Scholar 

  26. Harrington, T.: Is the current measurement criteria appropriate for selecting women who require transvaginal assessment of cervical length in a low-risk population? Sonography 1(2), 39–43 (2014)

    Article  Google Scholar 

  27. He, K., Gkioxari, G., Dollar, P. and Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  28. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  29. Hoskins, P.R., Martin, K., Thrush, A.: Diagnostic ultrasound: physics and equipment. CRC Press (2019)

    Google Scholar 

  30. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  31. Ibtehaz, N., Rahman, M.S.: Multiresunet: rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74–87 (2020)

    Google Scholar 

  32. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning, pages 448-456. PMLR, 2015

    Google Scholar 

  33. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)

    Google Scholar 

  34. Goldenberg, R.J., Meis, P., Mercer, B., Moawad, A., Das, A.: The length of the cervix and the risk of spontaneous premature delivery. N. Engl. J. Med. 334, 567–572 (1996)

    Google Scholar 

  35. Jaesung Jang and Chi Young Ahn: Industrial mathematics in ultrasound imaging. J. Korean Soc. Ind. Appl. Math. 20(3), 175–202 (2016)

    MathSciNet  MATH  Google Scholar 

  36. Jang, J., Park, Y., Kim, B., Lee, S.M., Kwon, J.Y., Seo, J.K.: Automatic estimation of fetal abdominal circumference from ultrasound images. IEEE J. Biomed. Health Inform. 22(5), 1512–1520 (2017)

    Google Scholar 

  37. Jardim, S.M.G.V.B., Figueiredo, M.A.T.: Segmentation of fetal ultrasound images. Ultrasound Med. Biol. 31(2), 243–250 (2005)

    Article  Google Scholar 

  38. Jegou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 11–19 (2017)

    Google Scholar 

  39. Jha, D., Smedsrud, P.H., Riegler, M.A., Johansen, D., De Lange, T., Halvorsen, P., Johansen, H.D.: Resunet++: an advanced architecture for medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM), pp. 225–2255. IEEE (2019)

    Google Scholar 

  40. Jin, J., Dundar, A., Culurciello, E.: Flattened convolutional neural networks for feedforward acceleration (2014). arXiv:1412.5474

  41. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision, pp. 694–711. Springer, Berlin (2016)

    Google Scholar 

  42. Kagan, K.O., Sonek, J.: How to measure cervical length. Ultrasound Obstet. Gynecol. 45(3), 358–362 (2015)

    Article  Google Scholar 

  43. Kehl, S., Schelkle, A., Thomas, A., Puhl, A., Meqdad, K., Tuschy, B., Berlit, S., Weiss, C., Bayer, C., Heimrich, J., et al.: Single deepest vertical pocket or amniotic fluid index as evaluation test for predicting adverse pregnancy outcome (safe trial): a multicenter, open-label, randomized controlled trial. Ultrasound Obstet. Gynecol. 47(6), 674–679 (2016)

    Article  Google Scholar 

  44. Kim, B., Kim, K.C., Park, Y., Kwon, J.Y., Jang, J., Seo, J.K.: Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images. Physiol. Meas. 39(10), 105007 (2018)

    Google Scholar 

  45. Kim, H.P., Lee, S.M., Kwon, J.Y., Park, Y., Kim, K.C., Seo, J.K.: Automatic evaluation of fetal head biometry from ultrasound images using machine learning. Physiol. Meas. 40(6), 065009 (2019)

    Google Scholar 

  46. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)

    Google Scholar 

  47. Kurjak, A.: Donald School Textbook of Ultrasound in Obstetrics & Gynaecology. JP Medical Ltd (2017)

    Google Scholar 

  48. Kuusela, P., Wennerholm, U.-B., Fadl, H., Wesstrom, J., Lindgren, P., Hagberg, H., Jacobsson, B., Valentin, L.: Second trimester cervical length measurements with transvaginal ultrasound: a prospective observational agreement and reliability study. Acta Obstet. Gynecol. Scand. 99(11), 1476–1485 (2020)

    Article  Google Scholar 

  49. Kwan, A., Dudley, J., Lantz, E.: Who really discovered snell’s law? Phys. World 15(4), 64 (2002)

    Article  Google Scholar 

  50. Sun, S., Kwon, H.Y., Kwon, J.Y., Yun, H.S., Park, S., Cho, H.C., Seo, J.K.: Deep learning-based automatic measurement of cervical length in transvaginal sonography, preprint (2022)

    Google Scholar 

  51. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  52. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)

    Google Scholar 

  53. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  54. Li, X.H.: Ultrasound scan conversion on TI’s C64x+ DSPs. Application Report SPRAB32, Texas Instruments (2009)

    Google Scholar 

  55. Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

    Google Scholar 

  56. Lin, X., Wang, F., Guo, L., Zhang, W.: An automatic key-frame selection method for monocular visual odometry of ground vehicle. IEEE Access 7, 70742–70754 (2019)

    Article  Google Scholar 

  57. Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B. and Sanchez, C.I.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Google Scholar 

  58. Livne, M., Rieger, J., Aydin, O.U., Taha, A.A., Akay, E.M., Kossen, T., Sobesky, J., Kelleher, J.D., Hildebrand, K., Frey, D., et al.: A u-net deep learning framework for high performance vessel segmentation in patients with cerebrovascular disease. Front. Neurosci. 13, 97 (2019)

    Google Scholar 

  59. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  60. Looney, P., Stevenson, G.N., Nicolaides, K.H., Plasencia, W., Molloholli, M., Natsis, S., Collins, S.L.: Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning. JCI Insight 3(11) (2018)

    Google Scholar 

  61. Lu, R., Shen, Y.: Image segmentation based on random neural network model and gabor filters. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 6464–6467. IEEE (2006)

    Google Scholar 

  62. Luntsi, G., Burabe, F.A., Ogenyi, P.A., Zira, J.D., Chigozie, N.I., Nkubli, F.B. and Dauda, M.: Sonographic estimation of amniotic fluid volume using the amniotic fluid index and the single deepest pocket in a resourcelimited setting. J. Med. Ultrasound 27(2), 63 (2019)

    Google Scholar 

  63. Malinger, G., Paladini, D., Haratz, K.K., Monteagudo, A., Pilu, G.L., Timor-Tritsch, I.E.: Isuog practice guidelines (updated): sonographic examination of the fetal central nervous system. part 1: performance of screening examination and indications for targeted neurosonography. Ultrasound Obstet. Gynecol. 56(3), 476–484 (2020)

    Google Scholar 

  64. Manning, F.A., Platt, L.D., Sipos, L.: Antepartum fetal evaluation: development of a fetal biophysical profile. Am. J. Obstet. Gynecol. 136(6), 787–795 (1980)

    Google Scholar 

  65. Martin, D.J., Wells, I.T., Goodwin, C.R.: Physics of ultrasound. Anaesth. Intensiv. Care Med. 16(3), 132–135 (2015)

    Google Scholar 

  66. Martin, J.A., Hamilton, B.E., Osterman, M.J., Driscoll, A.K.: Births: final data for 2019. National Vital Statistics Reports: From the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System 70(2), 1–51 (2021)

    Google Scholar 

  67. McLaughlin, R.A.: Randomized Hough transform: improved ellipse detection with comparison. Pattern Recognit. Lett. 19(3–4), 299–305 (1998)

    Google Scholar 

  68. Neubeck, A., Van Gool, L.: Efficient non-maximum suppression. In: 18th International Conference on Pattern Recognition (ICPR’06), vol. 3, pp. 850–855. IEEE (2006)

    Google Scholar 

  69. Ng, A., Swanevelder, J.: Resolution in ultrasound imaging. Contin. Educ. Anaesth. Critical Care Pain 11(5), 186–192 (2011)

    Article  Google Scholar 

  70. Ni, D., Yang, X., Chen, X., Chin, C.T., Chen, S., Heng, P.A., Li, S., Qin, J., Wang, T.: Standard plane localization in ultrasound by radial component model and selective search. Ultrasound Med. Biol. 40(11), 2728–2742 (2014)

    Google Scholar 

  71. Noble, W.S.: What is a support vector machine? Nat. Biotechnol. 24(12), 1565–1567 (2006)

    Google Scholar 

  72. Ouahabi, A., Taleb-Ahmed, A.: Deep learning for real-time semantic segmentation: application in ultrasound imaging. Pattern Recogn. Lett. 144, 27–34 (2021)

    Article  Google Scholar 

  73. Paladini, D., Malinger, G., Birnbaum, R., Monteagudo, A., Pilu, G., Salomon, L.J., Timor-Trirtsch, I.E.: Isuog practice guidelines (updated): sonographic examination of the fetal central nervous system. Part 2: performance of targeted neurosonography. Ultrasound Obstet. Gynecol. 57(4), 661–671 (2021)

    Google Scholar 

  74. Pathak, S.D., Haynor, D.R., Kim, Y.: Edge-guided boundary delineation in prostate ultrasound images. IEEE Trans. Med. Imaging 19(12), 1211–1219 (2000)

    Google Scholar 

  75. Phelan, J.P., Smith, C.V., Broussard, P., Small, M.: Amniotic fluid volume assessment with the four-quadrant technique at 36-42 weeks’ gestation. J. Reprod. Med. Obstet. Gynecol. 32(7), 540–542 (1987)

    Google Scholar 

  76. Ponomarev, G.V., Gelfand, M.S., Kazanov, M.D.: A multilevel thresholding combined with edge detection and shape-based recognition for segmentation of fetal ultrasound images. In: Proceedings of challenge US: Biometric Measurements from Fetal Ultrasound Images, ISBI, pp. 17–19 (2012)

    Google Scholar 

  77. Prasad, D.K., Leung, M.K., Quek, C.: Ellifit: an unconstrained, non-iterative, least squares based geometric ellipse fitting method. Pattern Recognit. 46(5), 1449–1465 (2013)

    Google Scholar 

  78. Pu, B., Li, K., Li, S., Zhu, N.: Automatic fetal ultrasound standard plane recognition based on deep learning and IIoT. IEEE Trans. Ind. Inform. (2021)

    Google Scholar 

  79. Purisch, S.E., Gyamfi-Bannerman, C.: Epidemiology of preterm birth. In: Seminars in Perinatology, vol. 41, pp. 387–391. Elsevier (2017)

    Google Scholar 

  80. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  81. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91–99 (2015)

    Google Scholar 

  82. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Berlin (2015)

    Google Scholar 

  83. Rueda, S., Fathima, S., Knight, C.L., Yaqub, M., Papageorghiou, A.T., Rahmatullah, B., Foi, A., Maggioni, M., Pepe, A., Tohka, J., et al.: Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans. Med. Imaging 33(4), 797–813 (2013)

    Google Scholar 

  84. Russakovsky, O., Deng, J., Hao, S., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  85. Rutherford, S.E., Smith, C.V., Phelan, J.P., Kawakami, K., Ahn, M.O.: Four-quadrant assessment of amniotic fluid volume. Interobserver and intraobserver variation. J. Reprod. Med. 32(8), 587–589 (1987)

    Google Scholar 

  86. Salomon, L.J., Alfirevic, Z., Da Silva Costa, F., Deter, R.L., Figueras, F., Ghi, T.A., Glanc, P., Khalil, A., Lee, W., Napolitano, R., et al.: Isuog practice guidelines: ultrasound assessment of fetal biometry and growth. Ultrasound Obstet. Gynecol. 53(6), 715–723 (2019)

    Google Scholar 

  87. Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., Rueckert, D.: Attention gated networks: learning to leverage salient regions in medical images. Med. Image Anal. 53, 197–207 (2019)

    Article  Google Scholar 

  88. Sotiriadis, A., Papatheodorou, S., Kavvadias, A., Makrydimas, G.: Transvaginal cervical length measurement for prediction of preterm birth in women with threatened preterm labor: a meta-analysis. Ultrasound Obstet. Gynecol.: Off. J. Int. Soc. Ultrasound Obstet. Gynecol. 35(1), 54–64 (2010)

    Google Scholar 

  89. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  90. Stebbing, R.V., McManigle, J.E.: A boundary fragment model for head segmentation in fetal ultrasound. In: Proceedings of Challenge US: Biometric Measurements from Fetal Ultrasound Images, ISBI, pp. 9–11 (2012)

    Google Scholar 

  91. Sun, S., Kwon, J.Y., Park, Y., Cho, H.C., Hyun, C.M., Seo, J.K.: Complementary network for accurate amniotic fluid segmentation from ultrasound images. IEEE Access 9, 108223–108235 (2021)

    Google Scholar 

  92. To, M.S., Skentou, C., Chan, C., Zagaliki, A., Nicolaides, K.H.: Cervical assessment at the routine 23-week scan: standardizing techniques. Ultrasound Obstet. Gynecol.: Off. J. Int. Soc. Ultrasound Obstet. Gynecol. 17(3), 217–219 (2001)

    Article  Google Scholar 

  93. Lingyun, W., Cheng, J.-Z., Li, S., Lei, B., Wang, T., Ni, D.: Fuiqa: fetal ultrasound image quality assessment with deep convolutional networks. IEEE Trans. Cybern. 47(5), 1336–1349 (2017)

    Article  Google Scholar 

  94. Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)

    Google Scholar 

  95. Lei, X., Oja, E., Kultanen, P.: A new curve detection method: randomized Hough transform (RHT). Pattern Recogn. Lett. 11(5), 331–338 (1990)

    Article  MATH  Google Scholar 

  96. Yin, S., Peng, Q., Li, H., Zhang, Z., You, X., Fischer, K., Furth, S.L., Tasian, G.E., Fan, Y.: Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks. Med. Image Anal. 60, 101602 (2020)

    Google Scholar 

  97. Yost, N.P., Bloom, S.L., Twickler, D.M., Leveno, K.J.: Pitfalls in ultrasonic cervical length measurement for predicting preterm birth. Obstet. Gynecol. 93(4), 510–516 (1999)

    Google Scholar 

  98. Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions (2015). arXiv:1511.07122

  99. Jinhua, Yu., Wang, Y., Chen, P.: Fetal ultrasound image segmentation system and its use in fetal weight estimation. Med. Biol. Eng. Comput. 46(12), 1227–1237 (2008)

    Article  Google Scholar 

  100. Yu, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 11(11), 1260–1270 (2002)

    Google Scholar 

  101. Zagzebski, J.A.: Essentials of ultrasound physics. Mosby (1996)

    Google Scholar 

  102. Zahedi-Spung, L.D., Raghuraman, N., Macones, G.A., Cahill, A.G., Rosenbloom, J.I.: Neonatal morbidity and mortality by mode of delivery in very preterm neonates. Am. J. Obstet. Gynecol. (2021)

    Google Scholar 

  103. Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24–26, 2017, Conference Track Proceedings. OpenReview.net (2017)

    Google Scholar 

  104. Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3–11. Springer, Berlin (2018)

    Google Scholar 

  105. Zongwei Zhou, Md., Siddiquee, M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2019)

    Article  Google Scholar 

  106. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)

    Google Scholar 

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Acknowledgements

This research was supported by Samsung Science & Technology Foundation (No. SRFC-IT1902-09). Cho and Seo were supported by a grant of the Korea Health Technology R &D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : HI20C0127).

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Authors and Affiliations

  1. AI Vision Group, Samsung Medison, Seoul, Korea

    Hyun Cheol Cho & Sung Wook Park

  2. School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, Korea

    Siyu Sun & Jin Keun Seo

  3. Department of Obstetrics and Gynecology, Institute of Women’s Life Medical Science, Yonsei University College of Medicine, Seoul, Korea

    Ja-Young Kwon

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  1. Hyun Cheol Cho
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  2. Siyu Sun
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  3. Sung Wook Park
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  4. Ja-Young Kwon
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  5. Jin Keun Seo
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Correspondence to Jin Keun Seo .

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  1. School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, Korea (Republic of)

    Jin Keun Seo

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Cho, H.C., Sun, S., Park, S.W., Kwon, JY., Seo, J.K. (2023). Artificial Intelligence for Fetal Ultrasound. In: Seo, J.K. (eds) Deep Learning and Medical Applications. Mathematics in Industry, vol 40. Springer, Singapore. https://doi.org/10.1007/978-981-99-1839-3_5

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