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A Kernel Density Estimation Based Quality Metric for Quality Assessment of Obstetric Ultrasound Video

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Trustworthy Machine Learning for Healthcare (TML4H 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13932))

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Abstract

Simplified ultrasound scanning protocols (sweeps) have been developed to reduce the high skill required to perform a regular obstetric ultrasound examination. However, without automated quality assessment of the video, the utility of such protocols in clinical practice is limited. An automated quality assessment algorithm is proposed that applies an object detector to detect fetal anatomies within ultrasound videos. Kernel density estimation is applied to the bounding box annotations to estimate a probability density function of certain bounding box properties such as the spatial and temporal position during the sweeps. This allows quantifying how well the spatio-temporal position of anatomies in a sweep agrees with previously seen data as a quality metric. The new quality metric is compared to other metrics of quality such as the confidence of the object detector model. The source code is available at: https://github.com/kwon-j/KDE-UltrasoundQA.

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Notes

  1. 1.

    The probability density/peak PDF value could be used, but this skews most of the values to be very low - as shown in Appendix Fig. 1.

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Acknowledgments

We thank the reviewers for their helpful feedback. Jong Kwon is supported by the EPSRC Center for Doctoral Training in Health Data Science (EP/S02428X/1). CALOPUS is supported by EPSRC GCRF grant (EP/R013853/1).

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Authors and Affiliations

  1. Department of Engineering Science, University of Oxford, Oxford, UK

    Jong Kwon, Jianbo Jiao & Julia Alison Noble

  2. School of Computer Science, University of Birmingham, Birmingham, UK

    Jianbo Jiao

  3. Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK

    Alice Self & Aris Papageorghiou

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  1. Jong Kwon
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  2. Jianbo Jiao
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  3. Alice Self
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  5. Aris Papageorghiou
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Correspondence to Jong Kwon .

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Editors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    Hao Chen

  2. Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    Luyang Luo

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Kwon, J., Jiao, J., Self, A., Alison Noble, J., Papageorghiou, A. (2023). A Kernel Density Estimation Based Quality Metric for Quality Assessment of Obstetric Ultrasound Video. In: Chen, H., Luo, L. (eds) Trustworthy Machine Learning for Healthcare. TML4H 2023. Lecture Notes in Computer Science, vol 13932. Springer, Cham. https://doi.org/10.1007/978-3-031-39539-0_12

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  • DOI: https://doi.org/10.1007/978-3-031-39539-0_12

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