[1] N.D.Hué, "Đại cương các phương pháp chân đoán hình ảnh," in Chẩn đoán hình ảnh, P. M. T. Nguyễn Duy Hué Ed. Hà Nội: Nhà xuất bản Giáo dục Việt Nam, 2010, ch.
1, pp. 9-35.
[2] B.v.H. Vương, "Siêu âm xác địnhtuôi thai," in Siêu âm sản khoa thực hành. Hồ Chí Minh: Nhà xuất bản Ý học Thành pho-H6 Chí Minh; 2014, ch. 4, pp. 69-77.
[3] B.v.H. Vương, “Cơ thể học thai’ nhi bình thường," in Siêu âm sản khoa thực hành. Hồ Chí Minh;-Nhà xuất bản Y hoc Thành phó Hồ Chí Minh, 2014, ch. 2, pp. 18-
21.
[4l B.v.H: Vương, "Siêu âm đánh giá sự rỗi loạn tăng trưởng của thai nhi," in Siêu
âm sản khoa thực hành. Hồ Chí Minh: Nhà xuất bản Y học Thành phó Hồ Chí Minh,
2014, ch. 5, pp. 79-88.
[5] :B.v.H. Vuong, "Hệ than kinh," insSiéu âm sản khoa thực hành. Hồ Chí Minh:
Nhà xuất bản Y hoc Thành phô Hồ Chí Minh, 2014, ch. 6, pp. 92-106.
[6] T.L. va den Heuvel;-D. de Bruijn, C. L. de Korte, and Br v. Ginneken. Automated measurement of fetal head..circumference using’ 2D ultrasound images, Zenodo. [Online]. Available: https://zenodo.org/record/1322001
[7] T. L. van-den Heuvel, D..de Bruijn, C. L. de Korte, and B. v. Ginneken,
"Automated measurement of fetal head circumference using 2D ultrasound images," PloS one, vol. 13, no. 8, p. e0200412, 2018.
[8] D.P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge, "Comparing images using the Hausdorff distance," JEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 850-863, 1993, doi: 10.1109/34.232073.
85
[9] V. Sundaresan, C. P. Bridge, C. Ioannou, and J. A. Noble, "Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks," in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 18-21 April 2017 2017, pp. 671-674, doi: 10.1109/ISBI.2017.7950609.
[10] Y. Li, R. Xu, J. Ohya, and H. Iwata, "Automatic fetal body and amniotic fluid segmentation from fetal ultrasound images-by encoder-decoder network with inner layers," in 2017 39th-Annual International Conference of the- IEEE Engineering in Medicine and Biology Sociéty (EMBC),-11-15. July 2017 2017, pp. 1485-1488, doi: 10.1109/EMBC.2017:8037116.
[11] S. Budd-er-al., "Confident head circumference measurement from ultrasound with real-time feedback for sonographers;" in. J/nternational Conference on Medical Image Computing and Computer-Assisted Intervention, 2019: Springer, pp. 683-691.
[12] OzRonneberger, P. Fischer, and T. Brox;."U-net: Convolutional networks for biomedical image | segmentation,"-in International Conference on Medical image computing and computer-assisted intervention, 2015: Springer, pp. 234-241.
[13] Z. Sobhaninia..et al., "Fetal Ultrasound Image Segmentaton for Measuring Biometric Parameters Using Multi-Task Deep Learning,” in 2019 4/]st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 23-27 July 2019 2019, pp. 6545-6548, doi: 10.1109/EMBC.2019.8856981.
[14] ~-A. Chaurasia and E. Culurciello, "LinkNet: Exploiting encoder representations for efficient semantic segmentation," in 2017 IEEE Visual Communications and Image Processing (VCIP), 10-13 Dec. 2017 2017, pp. 1-4, doi: 10.1109/VCIP.2017.8305148.
[15] R. Qu, G. Xu, C. Ding, W. Jia, and M. Sun, "Standard Plane Identification in Fetal Brain Ultrasound Scans Using a Differential Convolutional Neural Network," [EEE Access, vol. 8, pp. 83821-83830, 2020, doi: 10.1109/ACCESS.2020.2991845.
86
[16] Z.Sobhaninia, A. Emami, N. Karimi, and S. Samavi, "Localization of Fetal Head
in Ultrasound Images by Multiscale View and Deep Neural Networks," in 2020 25th International Computer Conference, Computer Society of Iran (CSICC), 1-2 Jan. 2020
2020, pp. 1-5, doi: 10.1109/CSICC49403.2020.9050094.
[17] J. Zhang, C. Petitjean, P. Lopez, and S. Ainouz, "Direct estimation of fetal head circumference from ultrasoundimages based-on regression CNN," in Medical Imaging with Deep Learning, 2020.
[18] A. Krizhevsky, I-Sutskever, andỉ. E. Hinton, "Imagenet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.
[19] D. PằKingma and J. Ba, "Adam: A method for stochastic optimization,” arXiv preprint arXiv: 1412.6980, 2014.
[20] Ne Qian; "On the momentum term in-gradient_descent learning algorithms," Neural networks, vol. 12, no. 1, pp. 145-151, 1999:
[21] K. Fukushima, SyMiyake, and T. Ito, "Neocognitron: A neural network model for a'mechamism of. visual-pattern recognition," JEEE-Transactions on Systems, Man, and Cybernetics, vol. SMC-13, no:5, pp. 826-834, 1983; doi: 10.1109/TSMC. 1983.6313076.
[22] Y.LeCun et al., "Backpropagation applied to handwritten zip code recognition," Neural computation,-vol. 1, no. 4, pp. 541-551, 1989.
[23] Y.Lecun, L. Bottou; Y. Bengio, and P. Haffner, "Gradient-based learning applied
to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324,
1998, doi: 10.1109/5.726791.
[24] C. Szegedy et al., "Going deeper with convolutions," in 20/5 IEEE Conference
on Computer Vision and Pattern Recognition (CVPR), 7-12 June 2015 2015, pp. 1-9, doi: 10.1109/CVPR.2015.7298594.
87
[25] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large- scale image recognition,” arXiv preprint arXiv: 1409. 1556, 2014.
[26] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 20/6 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27-30 June 2016 2016, pp. 770-778, doi: 10.1109/CVPR.2016.90.
[27] Stanford. "Convolutional Neural ẹetworks:-Architectures, Convolution/ Pooling Layers." https://cs23.1n.githuba6o/convolutional-networks/ (accéssed 27/12, 2020):
[28] G. Yadav, S. Maheshwari, and A. Agarwal, "Contrast limited adaptive histogram equalization based’ enhancement for real time video system," in 20/4 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 24-
27 Sept. 20142014, pp..2392-2397, doi: 10.1109/ICACCI.2014.6968381.
[29] A. W. Fitzgibbon, M. Pilu; and R. B. Fisher, "Direct least squares fitting of ellipses, “1n Proceedings. of 13th International-Conference_on Pattern Recognition, 25-
29 Aug. 1996 1996, vol. 1) pp. 253-257 vol.1, doi: 10.1109/ICPR.1996.546029.
[30] A.G. Howard et-al:;"Mobilenets: Efficient-convolutional neural networks for mobile vision applications,"“arXiv preprint arXiv: 1704.04561, 2017.
[31] M. Tan and Q:V. Le, "Efficientnet: Rethinking model scaling for convolutional neural networks," arXiv preprint arXiv: 1905.11946,2019.
[32]. S. Ioffe and C.-Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," arXiv preprint arXiv: 1502.03167, 2015.
[33] F. Yu and V. Koltun, "Multi-scale context aggregation by dilated convolutions," arXiv preprint arXiv:1511.07122, 2015.
[34] S. Vesal, N. Ravikumar, and A. Maier, "A 2D dilated residual U-Net for multi- organ segmentation in thoracic CT," arXiv preprint arXiv:1905.07710, 2019.
88