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Deep Learning Measurement Model to Segment the Nuchal Translucency Region for the Early Identification of Down Syndrome

Open Access
|May 2022

References

  1. [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
  2. [2] Nicolaides, K.H., Brizot, M.L., Snijders, R.J. (1994). Fetal nuchal translucency: Ultrasound screening for fetal trisomy in the first trimester of pregnancy. British Journal of Obstetrics and Gynecology, 101, 782-786. https://doi.org/10.1111/j.1471-0528.1994.tb11946.x10.1111/j.1471-0528.1994.tb11946.x7947527
  3. [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
  4. [4] Müller, M.A., Pajkrt, E., Bleker, O.P., Bonsel, G.J., Bilardo, C.M. (2004). Disappearance of enlarged nuchal translucency before 14 weeks’ gestation: Relationship with chromosomal abnormalities and pregnancy outcome. Ultrasound in Obstetrics & Gynecology, 24 (2), 169-174. https://doi.org/10.1002/uog.110310.1002/uog.110315287055
  5. [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
  6. [6] Deng, Y., Wang, Y., Chen, P., Yu, J. (2012). A hierarchical model for automatic nuchal translucency detection from ultrasound images. Computers in Biology and Medicine. 42 (6), 706-713. https://doi.org/10.1016/j.compbiomed.2012.04.00210.1016/j.compbiomed.2012.04.00222516299
  7. [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. [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. [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. [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. [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
  12. [12] Sciortino, G., Tegolo, D., Valenti, C. (2017). Automatic detection and measurement of nuchal translucency. Computers in Biology and Medicine. 82, 12-20. https://doi.org/10.1016/j.compbiomed.2017.01.00810.1016/j.compbiomed.2017.01.00828126630
  13. [13] Lee, Y.-B., Kim, M.-J., Kim, M.-H. (2007). Robust border enhancement and detection for measurement of fetal nuchal translucency in ultrasound images. Medical & Biological Engineering & Computing, 45 (11), 1143-1152. https://doi.org/10.1007/s11517-007-0225-710.1007/s11517-007-0225-717657519
  14. [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. [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
  16. [16] Saood, A., Hatem, I. (2021). COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Medical Imaging, 21 (1), 19. https://doi.org/10.1186/s12880-020-00529-510.1186/s12880-020-00529-5787036233557772
  17. [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
  18. [18] Badrinarayanan, V., Kendall, A., Cipolla, R. (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 39 (12), 2481-2495. https://doi.org/10.1109/TPAMI.2016.264461510.1109/TPAMI.2016.264461528060704
  19. [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. [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
  21. [21] Xin, M., Wang, Y. (2019). Research on image classification model based on deep convolution neural network. EURASIP Journal on Image and Video Processing, 2019 (1), 40. https://doi.org/10.1186/s13640-019-0417-810.1186/s13640-019-0417-8
  22. [22] Yadav, S.S., Jadhav, S.M. (2019). Deep convolutional neural network based medical image classification for disease diagnosis. Journal of Big Data, 6 (1), 113. https://doi.org/10.1186/s40537-019-0276-210.1186/s40537-019-0276-2
  23. [23] Fetal Medicine Foundation nuchal translucency. https://fetalmedicine.org
  24. [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
Language: English
Page range: 187 - 192
Submitted on: Nov 22, 2021
Accepted on: Apr 19, 2022
Published on: May 14, 2022
Published by: Slovak Academy of Sciences, Mathematical Institute
In partnership with: Paradigm Publishing Services
Publication frequency: 6 times per year

© 2022 Mary Christeena Thomas, Sridhar P. Arjunan, published by Slovak Academy of Sciences, Mathematical Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.