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Published December 30, 2023 | Version CC BY-NC-ND 4.0
Journal article Open

An Overview of Deep Learning Methods for Segmenting Thyroid Ultrasound Images

Creators

  • 1. Institute of Engineering and Technology, Chitkara University, Punjab, India.

Contributors

Contact person:

  • 1. Institute of Engineering and Technology, Chitkara University, Punjab, India.
  • 2. Department of Paediatrics, Endocrinology and Diabetes Unit, PGIMER, Chandigarh, India.

Description

Abstract: One of the various imaging modalities that is most frequently utilized in clinical practice is ultrasound (US). It is an emerging technology that has certain advantages along with disadvantages such as poor imaging quality and a lot of fluctuation. To aid in US diagnosis and/or to increase the objectivity and accuracy of such evaluation, effective automatic US image assessment techniques must be created from the perspective of image analysis. The most effective machine learning technology, notably in computer vision and general evaluation of images, has since been proven to belong to deep learning. Deep learning also has a huge potential for using US images for many automated activities. This paper quickly presents many well-known deep learning architectures before summarizing and delving into their applications in a number of distinct.

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Additional details

Identifiers

DOI
10.35940/ijaent.A9759.12101223
ISSN
2347-6389

Dates

Accepted
2023-12-15
Manuscript received on 04 November 2023 | Revised Manuscript received on 12 December 2023 | Manuscript Accepted on 15 December 2023 | Manuscript published on 30 December 2023

References

  • Vemulapalli L, SekharPC., Indian Journal of Applied Research, 9, p. 398 (2019).
  • Deng, L. and Yu, D., Foundations and trends in signal processing, 7(3–4), p.197 (2014). https://doi.org/10.1561/2000000039
  • Shen, D., Wu, G. and Suk, H.I., Annual review of biomedical engineering, 19, p.221 (2017). https://doi.org/10.1146/annurev-bioeng-071516-044442
  • Wang, G., IEEE, 4, p.8914 (2016). https://doi.org/10.1109/ACCESS.2016.2624938
  • Suzuki, K., Radiological physics and technology, 10(3), pp.257 (2017). https://doi.org/10.1007/s12194-017-0406-5
  • Vemulapalli L, SekharPC., Indian Journal of Applied Research, 9, p. 398 (2019).
  • Krizhevsky, A., Sutskever, I. and Hinton, G.E., Advances in neural information processing systems, 25, p.1097 (2012).
  • Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Martinez-Gonzalez, P. and Garcia-Rodriguez, J., Applied Soft Computing, 70, p.41 (2018). https://doi.org/10.1016/j.asoc.2018.05.018
  • Ronneberger, O., Fischer, P. and Brox, T., International Conference on Medical image computing and computer-assisted intervention p. 234 (2015). https://doi.org/10.1007/978-3-319-24574-4_28
  • Milletari, F., Navab, N. and Ahmadi, S.A., Fourth international conference on 3D vision (3DV), p.565 (2016).
  • N. I. of H.-C. Center. Chest X-ray NIHCC. [Online]. Available, https://nihcc.app.box.com/v/ChestXray-NIHCC [Accessed: 10-Nov-2021] (2017).
  • T. M. I. of T. (MIT)'s L. for C. Physiology. MIMIC-chest X-ray database (MIMIC-CXR) [Online]. Available, https://physionet.org/content/mimic-cxr/2.0.0/ [Accessed: 10-Nov-2021].
  • Reddy, U.M., Filly, R.A. and Copel, J.A., Obstetrics and gynecology, 112(1), p.145 (2008). https://doi.org/10.1097/01.AOG.0000318871.95090.d9
  • Haugen, B.R., Alexander, E.K., Bible, K.C., Doherty, G.M., Mandel, S.J., Nikiforov, Y.E., Pacini, F., Randolph, G.W., Sawka, A.M., Schlumberger, M. and Schuff, K.G., The American Thyroid Association guidelines task force on thyroid nodules and differentiated thyroid cancer, 26(1), pp.1(2016). https://doi.org/10.1089/thy.2015.0020
  • Gharib, H., Papini, E., Paschke, R., Duick, D.S., Valcavi, R., Hegedüs, L. and Vitti, P., Journal of endocrinological investigation, 33(5), p.287 (2010). https://doi.org/10.1007/BF03346587
  • Kwak, J.Y., Han, K.H., Yoon, J.H., Moon, H.J., Son, E.J., Park, S.H., Jung, H.K., Choi, J.S., Kim, B.M. and Kim, E.K., A step in establishing better stratification of cancer risk. Radiology, 260(3), p. 892 (2011). https://doi.org/10.1148/radiol.11110206
  • Park, J.Y., Lee, H.J., Jang, H.W., Kim, H.K., Yi, J.H., Lee, W. and Kim, S.H., A proposal for a thyroid imaging reporting and data system for ultrasound features of thyroid carcinoma. Thyroid, 19(11), p.1257 (2009). https://doi.org/10.1089/thy.2008.0021
  • Fotenos, A.F., Snyder, A.Z., Girton, L.E., Morris, J.C. and Buckner, R.L., Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology, 64(6), p.1032 (2005). https://doi.org/10.1212/01.WNL.0000154530.72969.11
  • Golan, R., Jacob, C. and Denzinger, J., International Joint Conference on Neural Networks (IJCNN), p. 243-(2016).
  • Milletari, F., Ahmadi, S.A., Kroll, C., Plate, A., Rozanski, V., Maiostre, J., Levin, J., Dietrich, O., Ertl-Wagner, B., Bötzel, K. and Navab, N., Computer Vision and Image Understanding, 164, p.92 (2017). https://doi.org/10.1016/j.cviu.2017.04.002
  • Perez, L. and Wang, J., The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621.
  • Shie, C.K., Chuang, C.H., Chou, C.N., Wu, M.H. and Chang, E.Y., Transfer representation learning for medical image analysis. 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), p.711 (2015). https://doi.org/10.1109/EMBC.2015.7318461
  • Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Martinez-Gonzalez, P. and Garcia-Rodriguez, J., Applied Soft Computing, 70, p. 41(2018). https://doi.org/10.1016/j.asoc.2018.05.018
  • Baratloo, A., Hosseini, M., Negida, A. and El Ashal, G., p.48 (2015).
  • Lalkhen, A.G. and McCluskey, A., Continuing education in anaesthesia critical care & pain, 8(6), p.221(2008). https://doi.org/10.1093/bjaceaccp/mkn041
  • Van Stralen, K.J., Stel, V.S., Reitsma, J.B., Dekker, F.W., Zoccali, C. and Jager, K.J., Kidney international, 75(12), p.1257 (2009). https://doi.org/10.1038/ki.2009.92
  • Csurka, G., Larlus, D., Perronnin, F. and Meylan, F., Bmvc, 27, p. 10 (2013).
  • Wong, H.B. and Lim, G.H., Proceedings of Singapore healthcare, 20(4), p.316 (2011). https://doi.org/10.1177/201010581102000411
  • Xu, Y., Wang, Y., Yuan, J., Cheng, Q., Wang, X. and Carson, P.L., Ultrasonics, 91, p.1 (2019). https://doi.org/10.1016/j.ultras.2018.07.006
  • Badea, M.S., Felea, I.I., Florea, L.M. and Vertan, C., arXiv preprint arXiv:1605.09612 (2016).
  • Kaur, J. and Jindal, A., International Journal of Computer Applications, 50(23), p.1 (2012). https://doi.org/10.5120/7959-0924
  • Poudel, P., Illanes, A., Sheet, D. and Friebe, M., Journal of healthcare engineering, (2018). https://doi.org/10.1155/2018/8087624
  • Shenoy, N.R. and Jatti, A., Indonesian Journal of Electrical Engineering and Computer Science, 21(3), p.1424 (2021). https://doi.org/10.11591/ijeecs.v21.i3.pp1424-1434
  • Garg, H. and Jindal, A., Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp.1 (2013).
  • Frannita, E.L., Nugroho, H.A., Nugroho, A. and Ardiyanto, I., 2nd International Conference on Imaging, Signal Processing and Communication (ICISPC), p. 79(2018).
  • Ying, X., Yu, Z., Yu, R., Li, X., Yu, M., Zhao, M. and Liu, K., International Conference on Neural Information Processing, p.373 (2018). https://doi.org/10.1007/978-3-030-04224-0_32
  • Kumbhakarna, V. M., Kulkarni, S. B., & Dhawale, A. D. (2020). NLP Algorithms Endowed f or Automatic Extraction of Information from Unstructured Free Text Reports of Radiology Monarchy. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 12, pp. 338–343). https://doi.org/10.35940/ijitee.l8009.1091220
  • Akila, Mrs. P. G., Batri, K., Sasi, G., & Ambika, R. (2019). Denoising of MRI Brain Images using Adaptive Clahe Filtering Method. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1s, pp. 91–95). https://doi.org/10.35940/ijeat.a1018.1091s19
  • Mounir, M., Redouane, E. B., Reda, M. M., Saad, E. M., & Abderaouf, E. H. (2019). Assessment of the Radiation Dose during 16 Slices CT Examinations. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 4, pp. 4652–4657). https://doi.org/10.35940/ijrte.d8388.118419