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Automatic and Efficient Standard Plane Recognition in Fetal Ultrasound Images via Multi-scale Dense Networks

  • Peiyao Kong
  • Dong Ni
  • Siping Chen
  • Shengli Li
  • Tianfu Wang
  • Baiying Lei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11076)

Abstract

The determination and interpretation of fetal standard planes (FSPs) in ultrasound examinations are the precondition and essential step for prenatal ultrasonography diagnosis. However, identifying multiple standard planes from ultrasound videos is a time-consuming and tedious task since there are only little differences between standard and non-standard planes in the adjacent scan frames. To address this challenge, we propose a general and efficient framework to detect several standard planes from ultrasound scan images or videos automatically. Specifically, a multi-scale dense networks (MSDNet) utilizing the multi-scale architecture and dense connection is exploited, which combines the fine level features from the shallow layers and coarse level features from the deep layers. Moreover, this MSDNet is resource efficient, and the cascade structure can adaptively select lightweight networks when test images are not complicated or computational resources limited. Experimental results based on our self-collected dataset demonstrate that the proposed method achieves a mean average precision (mAP) of 98.15% with half resources and double speeds in FSPs recognition task.

Keywords

Standard plane recognition Prenatal ultrasound images Resource efficient Multi-scale dense networks 

References

  1. 1.
    Li, J., et al.: Automatic fetal head circumference measurement in ultrasound using random forest and fast ellipse fitting. IEEE J. Biomed. Health Inform. 22, 215–223 (2018)CrossRefGoogle Scholar
  2. 2.
    Cai, Y., Sharma, H., Chatelain, P., Noble, J.: SonoEyeNet: standardized fetal ultrasound plane detection informed by eye tracking. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1475–1478. IEEE (2018)Google Scholar
  3. 3.
    Chen, H., et al.: Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 507–514. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24553-9_62CrossRefGoogle Scholar
  4. 4.
    Milletari, F., et al.: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput. Vis. Image Underst. 164, 92–102 (2017)CrossRefGoogle Scholar
  5. 5.
    Shen, D., Wu, G., Suk, H.-I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221–248 (2017)CrossRefGoogle Scholar
  6. 6.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  7. 7.
    Wu, L., Cheng, J.-Z., Li, S., Lei, B., Wang, T., Ni, D.: FUIQA: fetal ultrasound image quality assessment with deep convolutional networks. IEEE Trans. Cybern. 47, 1336–1349 (2017)CrossRefGoogle Scholar
  8. 8.
    Huang, G., Chen, D., Li, T., Wu, F., van der Maaten, L., Weinberger, K.: Multi-scale dense networks for resource efficient image classification. In: International Conference on Learning Representations (2018)Google Scholar
  9. 9.
    Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p. 3 (2017)Google Scholar
  10. 10.
    Baumgartner, C.F., et al.: SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans. Med. Imaging 36, 2204–2215 (2017)CrossRefGoogle Scholar
  11. 11.
    Yu, Z., et al.: A deep convolutional neural network-based framework for automatic fetal facial standard plane recognition. IEEE J. Biomed. Health Inform. 22, 874–885 (2018)CrossRefGoogle Scholar
  12. 12.
    Donahue, J., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, pp. 647–655 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurement and Ultrasound ImagingSchool of Biomedical Engineering, Shenzhen UniversityShenzhenChina
  2. 2.Department of UltrasoundAffiliated Shenzhen Maternal and Child Healthcare Hospital of Nanfang Medical UniversityShenzhenPeople’s Republic of China

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