[1] Asim, A., Kumar, A., Muthuswamy, S., Jain, S., Agarwal, S. (2015). Down syndrome: An insight of the disease. Journal of Biomedical Science, 22 (1), 41. https://dx.doi.org/10.1186%2Fs12929-015-0138-y10.1186/s12929-015-0138-y446463326062604
[3] Sciortino, G., Tegolo, D., Valenti, C. (2017). A non-supervised approach to locate and to measure the nuchal translucency by means of wavelet analysis and neural networks. In 2017 XXVI International Conference on Information, Communication and Automation Technologies (ICAT). IEEE, 1-7. https://doi.org/10.1109/ICAT.2017.817163110.1109/ICAT.2017.8171631
[5] Wright, D., Kagan, K.O., Molina, F.S., Gazzoni, A., Nicolaides, K.H. (2008) A mixture model of nuchal translucency thickness in screening for chromosomal defects. Ultrasound in Obstetrics & Gynecology, 31 (4), 376-383. https://doi.org/10.1002/uog.529910.1002/uog.529918383462
[7] Moratalla, J., Pintoffl, K., Minekawa, R., Lachmann, R., Wright, D., Nicolaides, K.H. (2010). Semiautomated system for measurement of nuchal translucency thickness. Ultrasound in Obstetrics & Gynecology, 36 (4), 412-416. https://doi.org/10.1002/uog.773710.1002/uog.773720617517
[8] Sonia, R., Shanthi, V. (2016). Early detection of Down syndrome marker by measuring fetal nuchal translucency thickness from ultrasound images during first trimester. Indian Journal of Science and Technology, 9 (21), 1-6. https://dx.doi.org/10.17485/ijst/2016/v9i21/9517410.17485/ijst/2016/v9i21/95174
[9] Nirmala, S., Palanisamy, V. (2009). Measurement of nuchal translucency thickness in first trimester ltrasound fetal images for detection of chromosomal abnormalities. In 2009 International Conference on Control, Automation, Communication and Energy Conservation. IEEE, 101-106. ISBN 978-1-4244-4789-3.
[10] Cho, H.Y., Kwon, J.-Y., Kim, Y.H., Lee, K.H., Kim, J., Kim, S.Y., Park, Y.W. (2012). Comparison of nuchal translucency measurements obtained using Volume NT(TM) and two- and three-dimensional ultrasound. Ultrasound in Obstetrics & Gynecology, 39 (2), 175-180. https://doi.org/10.1002/uog.899610.1002/uog.899621412924
[11] Deng, Y., Wang, Y., Chen, P. (2010). Automated detection of fetal nuchal translucency based on hierarchical structural model. In 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 78-84. https://doi.org/10.1109/CBMS.2010.604261810.1109/CBMS.2010.6042618
[14] Omar, A. (2019). Lung CT parenchyma segmentation using VGG-16 based SegNet model. International Journal of Computer Applications, 178 (44), 10-13. http://dx.doi.org/10.5120/ijca201991930810.5120/ijca2019919308
[15] Sahiner, B., Pezeshk, A., Hadjiiski, L.M., Wang, X., Drukker, K., Cha, K.H., Summers, R.M., Giger, M.L. (2019). Deep learning in medical imaging and radiation therapy. Medical Physics. 46 (1), e1-e36. https://doi.org/10.1002/mp.1326410.1002/mp.1326430367497
[17] Singh, S., Ho-Shon, K., Karimi, S., Hamey, L. (2018). Modality classification and concept detection in medical images using deep transfer learning. In 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 1-9. https://doi.org/10.1109/IVCNZ.2018.863480310.1109/IVCNZ.2018.8634803
[19] Sivakumar, R., Gayathri, M.K., Nedumaran, D. (2010). Speckle filtering of ultrasound B-Scan Images - a comparative study between spatial and diffusion filters. In 2010 IEEE Conference on Open Systems (ICOS 2010). IEEE, 80-85. https://doi.org/10.1109/ICOS.2010.572006810.1109/ICOS.2010.5720068
[20] Sivakumar, R., Gayathri, M.K., Nedumaran, D. (2010). Speckle filtering of ultrasound B-Scan images - a comparative study of single scale spatial adaptive filters, multiscale filter and diffusion filters. International Journal of Engineering and Technology. 2 (6), 514-523. http://dx.doi.org/10.7763/IJET.2010.V2.17410.7763/IJET.2010.V2.174
[24] Otsuka, K. (2020). Medical image segmentation using SegNet. MATLAB Central File Exchange, https://www.mathworks.com/matlabcentral/fileexchange/66448-medical-image-segmentation-using-segnet