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Fetal Ultrasound Image Segmentation for Automatic Head Circumference Biometry Using Deeply Supervised Attention-Gated V-Net

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Abstract

Automatic computerized segmentation of fetal head from ultrasound images and head circumference (HC) biometric measurement is still challenging, due to the inherent characteristics of fetal ultrasound images at different semesters of pregnancy. In this paper, we proposed a new deep learning method for automatic fetal ultrasound image segmentation and HC biometry: deeply supervised attention-gated (DAG) V-Net, which incorporated the attention mechanism and deep supervision strategy into V-Net models. In addition, multi-scale loss function was introduced for deep supervision. The training set of the HC18 Challenge was expanded with data augmentation to train the DAG V-Net deep learning models. The trained models were used to automatically segment fetal head from two-dimensional ultrasound images, followed by morphological processing, edge detection, and ellipse fitting. The fitted ellipses were then used for HC biometric measurement. The proposed DAG V-Net method was evaluated on the testing set of HC18 (n = 355), in terms of four performance indices: Dice similarity coefficient (DSC), Hausdorff distance (HD), HC difference (DF), and HC absolute difference (ADF). Experimental results showed that DAG V-Net had a DSC of 97.93%, a DF of 0.09 ± 2.45 mm, an AD of 1.77 ± 1.69 mm, and an HD of 1.29 ± 0.79 mm. The proposed DAG V-Net method ranks fifth among the participants in the HC18 Challenge. By incorporating the attention mechanism and deep supervision, the proposed method yielded better segmentation performance than conventional U-Net and V-Net methods. Compared with published state-of-the-art methods, the proposed DAG V-Net had better or comparable segmentation performance. The proposed DAG V-Net may be used as a new method for fetal ultrasound image segmentation and HC biometry. The code of DAG V-Net will be made available publicly on https://github.com/xiaojinmao-code/.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their insightful and valuable comments and suggestions.

Funding

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 11804013, 61871005, 61801312, and 71661167001), the Beijing Natural Science Foundation (Grant No. 4184081), the International Research Cooperation Seed Fund of Beijing University of Technology (Grant No. 2018A15), the Basic Research Fund of Beijing University of Technology, and the Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation.

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Author notes

  1. Yan Zeng and Po-Hsiang Tsui have equally contributed in this work.

Authors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing, China

    Yan Zeng, Zhuhuang Zhou & Shuicai Wu

  2. Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan

    Po-Hsiang Tsui

  3. Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan

    Po-Hsiang Tsui

  4. Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan

    Po-Hsiang Tsui

  5. College of Biomedical Engineering, Capital Medical University, Beijing, China

    Weiwei Wu

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  1. Yan Zeng
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Correspondence to Zhuhuang Zhou or Shuicai Wu.

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Conflict of Interest

The authors declare that they have no competing interests.

Ethical Approval

This retrospective study used fetal ultrasound images provided by the HC18 Challenge, which were collected from the database of the Department of Obstetrics of the Radboud University Medical Center, Nijmegen, the Netherlands, in accordance with the local ethics committee (CMO Arnhem-Nijmegen). All data were anonymized according to the tenets of the Declaration of Helsinki.

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This study used retrospective data provided by the HC18 Challenge, so informed consent was waived.

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Zeng, Y., Tsui, PH., Wu, W. et al. Fetal Ultrasound Image Segmentation for Automatic Head Circumference Biometry Using Deeply Supervised Attention-Gated V-Net. J Digit Imaging 34, 134–148 (2021). https://doi.org/10.1007/s10278-020-00410-5

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