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Automated Maternal Fetal Ultrasound Image Identification Using a Hybrid Vision Transformer Model

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Pattern Recognition (ICPR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15311))

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Abstract

Ultrasound (US) technology has revolutionized prenatal care by offering noninvasive, real-time visualization of maternal-fetal anatomy. The accurate classification of maternal-fetal US planes is a critical segment of effective prenatal diagnosis. However, the inherent inter-class variance among different fetal US images presents a significant hurdle, making fetal anatomy detection a laborious and time-consuming task, even for experienced sonographers. This paper proposes a novel approach using a Hybrid Vision Transformer (H-ViT) for automated fetal anatomical plane classification to address these challenges. The proposed method utilizes hierarchical features extracted from DenseNet-121, which are then inputted into the vision transformer to analyze complex spatial relationships and patterns within fetal US images. By incorporating both global and local features, the proposed method enhances feature discriminability, thus alleviating low inter-class variance. The effectiveness of the H-ViT is evaluated using the largest publicly available maternal-fetal US image dataset. The experimental results rigorously demonstrate the superiority of our approach, achieving an accuracy of 96.60% compared to other state-of-the-art techniques.

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References

  1. Wells, P.N.: Ultrasound imaging. Physics in Medicine & Biology 51(13), R83 (2006)

    Article  Google Scholar 

  2. Levine, D.: Ultrasound versus magnetic resonance imaging in fetal evaluation. Top. Magn. Reson. Imaging 12(1), 25–38 (2001)

    Article  Google Scholar 

  3. Huang, Q., Zeng, Z., et al.: A review on real-time 3D ultrasound imaging technology. BioMed Research International 2017 (2017)

    Google Scholar 

  4. Meng, L., Zhao, D., Yang, Z., Wang, B.: Automatic display of fetal brain planes and automatic measurements of fetal brain parameters by transabdominal three-dimensional ultrasound. J. Clin. Ultrasound 48(2), 82–88 (2020)

    Article  Google Scholar 

  5. Hadlock, F.P., Harrist, R., 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)

    Article  Google Scholar 

  6. Turan, S., Miller, J., Baschat, A.A.: Integrated testing and management in fetal growth restriction. In: Seminars in Perinatology. vol. 32, pp. 194–200. Elsevier (2008)

    Google Scholar 

  7. Nicolaides, K.H., Syngelaki, A., Ashoor, G., Birdir, C., Touzet, G.: Noninvasive prenatal testing for fetal trisomies in a routinely screened first-trimester population. Am. J. Obstet. Gynecol. 207(5), 374-e1 (2012)

    Article  Google Scholar 

  8. Burgos-Artizzu, X.P., Coronado-Gutierrez, D., Valenzuela-Alcaraz, B., Bonet-Carne, E., Eixarch, E., Crispi, F., Gratacós, E.: FETAL_PLANES_DB: Common maternal-fetal ultrasound images (Jun 2020), https://doi.org/10.5281/zenodo.3904280

  9. Krishna, T.B., Kokil, P.: Standard fetal ultrasound plane classification based on stacked ensemble of deep learning models. Expert Syst. Appl. 238, 122153 (2024)

    Article  Google Scholar 

  10. Fiorentino, M.C., Villani, F.P., Di Cosmo, M., Frontoni, E., Moccia, S.: A review on deep-learning algorithms for fetal ultrasound-image analysis. Med. Image Anal. 83, 102629 (2023)

    Article  Google Scholar 

  11. Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S.X., Ni, D., Wang, T.: Deep learning in medical ultrasound analysis: A review. Engineering 5(2), 261–275 (2019)

    Article  Google Scholar 

  12. Burgos-Artizzu, X.P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B., Bonet-Carne, E., Eixarch, E., Crispi, F., Gratacós, E.: Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci. Rep. 10(1), 10200 (2020)

    Article  Google Scholar 

  13. Sridar, P., Kumar, A., Quinton, A., Nanan, R., Kim, J., Krishnakumar, R.: Decision fusion-based fetal ultrasound image plane classification using convolutional neural networks. Ultrasound in Medicine & Biology 45(5), 1259–1273 (2019)

    Article  Google Scholar 

  14. Rasheed, K., Junejo, F., Malik, A., Saqib, M.: Automated fetal head classification and segmentation using ultrasound video. IEEE Access 9, 160249–160267 (2021)

    Article  Google Scholar 

  15. Krishna, T.B., Kokil, P.: Automated detection of common maternal fetal ultrasound planes using deep feature fusion. In: IEEE 19th India Council International Conference (INDICON). pp. 1–5. Kochi, India (2022)

    Google Scholar 

  16. Yu, Z., Tan, E.L., Ni, D., Qin, J., Chen, S., Li, S., Lei, B., Wang, T.: A deep convolutional neural network-based framework for automatic fetal facial standard plane recognition. IEEE J. Biomed. Health Inform. 22(3), 874–885 (2017)

    Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. Yang, X., Ni, D., Qin, J., Li, S., Wang, T., Chen, S., Heng, P.A.: Standard plane localization in ultrasound by radial component. In: 11th International Symposium on Biomedical Imaging (ISBI). pp. 1180–1183. IEEE (2014)

    Google Scholar 

  19. Lei, B., Zhuo, L., Chen, S., Li, S., Ni, D., Wang, T.: Automatic recognition of fetal standard plane in ultrasound image. In: 11th International Symposium on Biomedical Imaging (ISBI). pp. 85–88. IEEE (2014)

    Google Scholar 

  20. Pu, B., Li, K., Li, S., Zhu, N.: Automatic fetal ultrasound standard plane recognition based on deep learning and IIoT. IEEE Trans. Industr. Inf. 17(11), 7771–7780 (2021)

    Article  Google Scholar 

  21. Krishna, T.B., Kokil, P.: Automated classification of common maternal fetal ultrasound planes using multi-layer perceptron with deep feature integration. Biomed. Signal Process. Control 86, 105283 (2023)

    Article  Google Scholar 

  22. Sindhu, K.G., R, A.: Ensemble-based advancements in maternal fetal plane and brain plane classification for enhanced prenatal diagnosis. International Journal of Information Technology pp. 1–17 (2024)

    Google Scholar 

  23. Krishna, T.B., Kokil, P.: Integration of a deep convolutional neural network with adaptive channel weight technique for automated identification of standard fetal biometry planes. IEEE Trans. Instrum. Meas. 73, 1–11 (2024)

    Article  Google Scholar 

  24. Ma’Sum, M.A., Jatmiko, W., Tawakal, M.I., Al Afif, F.: Automatic fetal organs detection and approximation in ultrasound image using boosting classifier and Hough transform. In: International Conference on Advanced Computer Science and Information System. pp. 460–467. IEEE (2014)

    Google Scholar 

  25. Yang, S., Xiao, W., Zhang, M., Guo, S., Zhao, J., Shen, F.: Image data augmentation for deep learning: A survey. arXiv preprint arXiv:2204.08610 (2022)

  26. Thabtah, F., Hammoud, S., Kamalov, F., Gonsalves, A.: Data imbalance in classification: Experimental evaluation. Inf. Sci. 513, 429–441 (2020)

    Article  MathSciNet  Google Scholar 

  27. 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 

  28. Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R., Mittal, A.: Pneumonia detection using CNN based feature extraction. In: International Conference on Electrical, Computer and Communication Technologies (ICECCT). pp. 1–7. IEEE (2019)

    Google Scholar 

  29. Chen, C.F.R., Fan, Q., Panda, R.: Crossvit: Cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 357–366 (2021)

    Google Scholar 

  30. Angelina, C.L., Chou, Y.K., Lee, T.C., Kongkam, P., Han, M.L., Wang, H.P., Chang, H.T.: Hybrid vision transformer for classification of pancreatic cystic lesions on confocal laser endomicroscopy videos. In: International Conference on Consumer Electronics-Taiwan (ICCE-Taiwan). pp. 47–48. IEEE (2023)

    Google Scholar 

  31. Duarte-Salazar, C.A., Castro-Ospina, A.E., Becerra, M.A., Delgado-Trejos, E.: Speckle noise reduction in ultrasound images for improving the metrological evaluation of biomedical applications: An overview. IEEE Access 8, 15983–15999 (2020)

    Article  Google Scholar 

  32. Tuthill, T., Sperry, R., Parker, K.: Deviations from Rayleigh statistics in ultrasonic speckle. Ultrason. Imaging 10(2), 81–89 (1988)

    Article  Google Scholar 

  33. Ghabri, H., Alqahtani, M.S., Ben Othman, S., Al-Rasheed, A., Abbas, M., Almubarak, H.A., Sakli, H., Abdelkarim, M.N.: Transfer learning for accurate fetal organ classification from ultrasound images: A potential tool for maternal healthcare providers. Sci. Rep. 13(1), 17904 (2023)

    Article  Google Scholar 

  34. Sendra-Balcells, C., Campello, V.M., Torrents-Barrena, J., Ahmed, Y.A., Elattar, M., Ohene-Botwe, B., Nyangulu, P., Stones, W., Ammar, M., Benamer, L.N., et al.: Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries. Sci. Rep. 13(1), 2728 (2023)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Advanced Signal and Image Processing (ASIP) Lab, Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, 600127, India

    Thunakala Bala Krishna, Ajay Kumar Reddy Poreddy & Priyanka Kokil

  2. Amrita School of Computing, Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Chennai, Tamil Nadu, India

    Kolla Gnapika Sindhu

Authors
  1. Thunakala Bala Krishna
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  2. Ajay Kumar Reddy Poreddy
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  3. Kolla Gnapika Sindhu
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  4. Priyanka Kokil
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Corresponding author

Correspondence to Priyanka Kokil .

Editor information

Editors and Affiliations

  1. University of Salford, Salford, Lancashire, UK

    Apostolos Antonacopoulos

  2. IIT Bombay, Powai, Mumbai, Maharashtra, India

    Subhasis Chaudhuri

  3. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

  4. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  5. IIT Kharagpur, Kharagpur, West Bengal, India

    Saumik Bhattacharya

  6. Indian Statistical Institute, Kolkata, West Bengal, India

    Umapada Pal

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Krishna, T.B., Poreddy, A.K.R., Sindhu, K.G., Kokil, P. (2025). Automated Maternal Fetal Ultrasound Image Identification Using a Hybrid Vision Transformer Model. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15311. Springer, Cham. https://doi.org/10.1007/978-3-031-78195-7_17

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  • DOI: https://doi.org/10.1007/978-3-031-78195-7_17

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  • Online ISBN: 978-3-031-78195-7

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