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Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images

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Abstract

Measurement of anatomical structures from ultrasound images requires the expertise of experienced clinicians. Moreover, there are artificial factors that make an automatic measurement complicated. In this paper, we aim to present a novel end-to-end deep learning network to automatically measure the fetal head circumference (HC), biparietal diameter (BPD), and occipitofrontal diameter (OFD) length from 2D ultrasound images. Fully convolutional neural networks (FCNNs) have shown significant improvement in natural image segmentation. Therefore, to overcome the potential difficulties in automated segmentation, we present a novelty FCNN and add a regression branch for predicting OFD and BPD in parallel. In the segmentation branch, a feature pyramid inside our network is built from low-level feature layers for a variety of fetal head in ultrasound images, which is different from traditional feature pyramid building methods. In order to select the most useful scale and reduce scale noise, attention mechanism is taken for the feature’s filter. In the regression branch, for the accurate estimation of OFD and BPD length, a new region of interest (ROI) pooling layer is proposed to extract the elliptic feature map. We also evaluate the performance of our method on large dataset: HC18. Our experimental results show that our method can achieve better performance than the existing fetal head measurement methods.

Deep Neural Network for Fetal Head Measurement

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Notes

  1. https://hc18.grand-challenge.org/

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Authors and Affiliations

  1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Peixuan Li, Huaici Zhao, Pengfei Liu & Feidao Cao

  2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China

    Peixuan Li, Huaici Zhao, Pengfei Liu & Feidao Cao

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Peixuan Li, Pengfei Liu & Feidao Cao

  4. Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, 110016, China

    Peixuan Li, Huaici Zhao, Pengfei Liu & Feidao Cao

  5. Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang, 110016, China

    Peixuan Li, Huaici Zhao, Pengfei Liu & Feidao Cao

Authors
  1. Peixuan Li
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  2. Huaici Zhao
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  3. Pengfei Liu
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  4. Feidao Cao
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Correspondence to Huaici Zhao.

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Li, P., Zhao, H., Liu, P. et al. Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images. Med Biol Eng Comput 58, 2879–2892 (2020). https://doi.org/10.1007/s11517-020-02242-5

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  • DOI: https://doi.org/10.1007/s11517-020-02242-5

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