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Identification of Crown and Rump in First-Trimester Ultrasound Images Using Deep Convolutional Neural Network

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Applied Intelligence and Informatics (AII 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1724))

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Abstract

First-Trimester Ultrasound scans provide invaluable insight into early pregnancies. The scan is used to estimate the gestational age by providing a measurement of the Crown to Rump Length (CRL), it is a crucial scan as it informs obstetric practitioners of the optimal timing for any necessary interventions at the earliest point. Inter-observer variation creates problems for Obstetric Practitioners as any variation in the measurement of the CRL can carry complications to the fetus’ health. Existing machine learning systems to solve this problem are limited; this work details the creation of a machine learning pipeline that implements three Convolutional Neural Networks models (CNNs) to help identify the Crown and Rump regions in First-Trimester Ultrasound Images. The system segments the fetus in the image using a U-Net Model. The segmented image is then subject to an image classification model that implements a pre-trained CNN model, namely, VGG-16. This model is used to classify the segmented images into ‘Good’ and ‘Bad’. Finally, the segmented images are entered into a pre-trained ResNet34 model that identifies the Crown and Rump regions. This can be used by obstetric practitioners to provide an accurate CRL of the fetus and to comment on the actual development of the fetus from the First-trimester Ultrasound images. The system will mitigate issues with the estimation of the gestational age and reduce the inter-observer variations.

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Acknowledgments

Data used in this study was obtained from the Fetal Medicine Centre at Southend University Hospital.

Author information

Authors and Affiliations

  1. Department of Computer Science, Nottingham Trent University, Clifton, Nottingham, NG11 8NS, UK

    Samuel Sutton & Mufti Mahmud

  2. Medical Technologies Innovation Facility, Nottingham Trent University, Clifton, Nottingham, NG11 8NS, UK

    Mufti Mahmud

  3. Computing and Informatics Research Center, Nottingham Trent University, Clifton, Nottingham, NG11 8NS, UK

    Mufti Mahmud

  4. Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Rishi Singh

  5. Kypros Nicholaides Fetal Medicine Centre, Southend University Hospital, Westcliff-on-Sea, Essex, SS0 0RY, UK

    Luis Yovera

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  1. Samuel Sutton
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  2. Mufti Mahmud
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  3. Rishi Singh
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  4. Luis Yovera
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Corresponding author

Correspondence to Mufti Mahmud .

Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Cosimo Ieracitano

  3. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  4. University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Nadia Mammone

  5. University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

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Sutton, S., Mahmud, M., Singh, R., Yovera, L. (2022). Identification of Crown and Rump in First-Trimester Ultrasound Images Using Deep Convolutional Neural Network. In: Mahmud, M., Ieracitano, C., Kaiser, M.S., Mammone, N., Morabito, F.C. (eds) Applied Intelligence and Informatics. AII 2022. Communications in Computer and Information Science, vol 1724. Springer, Cham. https://doi.org/10.1007/978-3-031-24801-6_17

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