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Head circumference measurement with deep learning approach based on multi-scale ultrasound images

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Abstract

Checking up on the health of the fetus during pregnancy is an important issue that should be taken into account. Ultrasound imaging, as one useful medical imaging technique, helps specialists to monitor and diagnose the natural fetal growth process. Head Circumference (HC) measurement is considered as one of the remarkable criteria in fetus health assessment. In this study, I proposed a model to automatically extract the fetal head parameters based on three main phases, including pre-processing, fetal head extraction, and post-processing. In the fetal head extraction phase, I suggested a deep learning-based network based on multi-scale ultrasound images to diagnose and segment the fetal head using a loss function \({L}_{DeepLinkNet}\) weights the fetal head border pixels during algorithm learning to improve the performance of fetal head parameter extraction methods by reducing the number of required parameters and training time. According to experimental results, the accuracy of segmentation and the power of network training have significantly increased, which leads my proposed method has better performance in terms of two evaluation criteria for HC measurement such as Hausdorff, and the absolute difference compared to other previous methods, and better efficiency than the Link-Net model in all criteria due to multi-scalability and fewer network layers.

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References

  1. Amin J et al (2019) A new approach for brain tumor segmentation and classification based on score level fusion using transfer learning. J Med Syst 43(11):326

    Article  MathSciNet  Google Scholar 

  2. Cerrolaza JJ et al (2017) Fetal skull segmentation in 3D ultrasound via structured geodesic random forest. In: Fetal, infant and ophthalmic medical image analysis. Springer, Berlin, pp 25–32

  3. Cerrolaza JJ et al (2018) Deep learning with ultrasound physics for fetal skull segmentation. Biomedical Imaging International Symposium 2018. IEEE

  4. Cerrolaza JJ et al (2018) 3d fetal skull reconstruction from 2dus via deep conditional generative networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Berlin

  5. Chaurasia A, Culurciello E (2017) Linknet: Exploiting encoder representations for efficient semantic segmentation. Visual Communications and Image Processing (VCIP)

  6. Chen H-C et al (2012)Registration-based segmentation of three-dimensional ultrasound images for quantitative measurement of fetal craniofacial structure. Ultrasound Med Biol 38(5):811–823

    Article  Google Scholar 

  7. Chen K, Franko K, Sang R (2021) Structured model pruning of convolutional networks on tensor processing units. arXiv preprint arXiv:2107.04191

  8. Feng S, Zhou KS, Lee W (2012) Automatic fetal weight estimation using 3D ultrasonography in Medical Imaging 2012: Computer-Aided Diagnosis. International Society for Optics and Photonics

  9. Gomez A et al (2017) Fast registration of 3D fetal ultrasound images using learned corresponding salient points. In: Fetal, infant and ophthalmic medical image analysis. Springer, Berlin, pp 33–41

  10. Heuvel TL et al (2018) Automated measurement of fetal head circumference using 2D ultrasound images. PLoS One 13(8):e0200412

    Article  Google Scholar 

  11. Jardim SM, Figueiredo MA (2005) Segmentation of fetal ultrasound images. Ultrasound Med Biol 31(2):243–250

    Article  Google Scholar 

  12. Kim HP et al (2019) Automatic evaluation of fetal head biometry from ultrasound images using machine learning. Physiol Meas 40(6):065009

    Article  Google Scholar 

  13. Li J et al (2017) Automatic fetal head circumference measurement in ultrasound using random forest and fast ellipse fitting. IEEE J Biomed Health Inf 22(1):215–223

    Article  Google Scholar 

  14. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  15. Loughna P et al (2009) Fetal size and dating: charts recommended for clinical obstetric practice. Ultrasound 17(3):160–166

    Article  Google Scholar 

  16. Milletari F, Navab N, Ahmadi S-A(2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: fourth international conference on 3D vision (3DV). IEEE

  17. Namburete AI et al (2015)Learning-based prediction of gestational age from ultrasound images of the fetal brain. Med Image Anal 21(1):72–86

    Article  Google Scholar 

  18. Namburete AI et al (2018)Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning. Med Image Anal 46:1–14

    Article  Google Scholar 

  19. Perez-Gonzalez J et al (2014) Automatic fetal head measurements from ultrasound images using optimal ellipse detection and texture maps. in VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 2015. Springer, Berlin

  20. Ponomarev GV, Gelfand MS, Kazanov MD (2012) A multilevel thresholding combined with edge detection and shape-based recognition for segmentation of fetal ultrasound images. In: Proceedings of Challenge US: Biometric Measurements from Fetal Ultrasound Images, ISBI 2012. Citeseer

  21. Rafiei S et al (2018) Liversegmentation in CT images using three dimensional to two dimensional fullyconvolutional network. In: 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE

  22. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, Berlin

  23. Rueda S et al (2013) Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans Med Imaging 33(4):797–813

    Article  Google Scholar 

  24. Schmidt U et al (2014) Finding the most accurate method to measure head circumference for fetal weight estimation. Eur J Obstet Gynecol Reprod Biol 178:153–156

    Article  Google Scholar 

  25. Shrimali V, Anand R, Kumar V (2009) Improved segmentation of ultrasound images for fetal biometry, using morphological operators. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2009. IEEE

  26. Sobhaninia Z et al (2018) Brain tumor segmentation using deep learning by type specific sorting of images. arXiv preprint arXiv:1809.07786

  27. Sobhaninia Z et al (2019) Fetal ultrasound image segmentation for measuring biometric parameters using multi-task deep learning. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE

  28. Sobhaninia Z et al (2020) 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). IEEE

  29. Sobhaninia Z et al (2020) Brain tumor segmentation by cascaded deep neural networks using multiple image scales. arXiv preprint arXiv:2002.01975

  30. Torrents-Barrena J et al (2019) Segmentation and classification in MRI and US fetal imaging: recent trends and future prospects. Med Image Anal 51:61–88

    Article  Google Scholar 

  31. Wang J et al (2021) A review of deep learning on medical image analysis. Mob Netw Appl 26(1):351–380

    Article  Google Scholar 

  32. Wu L et al (2017) Cascaded fully convolutional networks for automatic prenatal ultrasound image segmentation. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE

  33. Yang X et al (2020) Hybrid attention for automatic segmentation of whole fetal head in prenatal ultrasound volumes. Comput Methods Programs Biomed 194:105519

    Article  Google Scholar 

  34. Zeng Y et al (2021) Fetal ultrasound image segmentation for automatic head circumference biometry using deeply supervised attention-gated V-Net. J Digit Imaging 34(1):134–148

  35. Zhang J et al (2020) Direct estimation of fetal head circumference from ultrasound images based on regression CNN. In: Medical Imaging with Deep Learning

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  1. Department of Electrical and Computer Engineering, Waterloo University, Waterloo, ON, Canada

    Seyedeh Moloud Amini

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  1. Seyedeh Moloud Amini
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Correspondence to Seyedeh Moloud Amini.

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Amini, S.M. Head circumference measurement with deep learning approach based on multi-scale ultrasound images. Multimed Tools Appl 81, 32981–32993 (2022). https://doi.org/10.1007/s11042-022-13107-4

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  • DOI: https://doi.org/10.1007/s11042-022-13107-4

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