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Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Fetal brain abnormalities are some of the most common congenital malformations that may associated with syndromic and chromosomal malformations, and could lead to neurodevelopmental delay and mental retardation. Early prenatal detection of brain abnormalities is essential for informing clinical management pathways and consulting for parents. The purpose of this research is to develop computer-aided diagnosis algorithms for five common fetal brain abnormalities, which may provide assistance to doctors for brain abnormalities detection in antenatal neurosonographic assessment.

Methods

We applied a classifier to classify images of fetal brain standard planes (transventricular and transcerebellar) as normal or abnormal. The classifier was trained by image-level labeled images. In the first step, craniocerebral regions were segmented from the ultrasound images. Then, these segmentations were classified into four categories. Last, the lesions in the abnormal images were localized by class activation mapping.

Results

We evaluated our algorithms on real-world clinical datasets of fetal brain ultrasound images. We observed that the proposed method achieved a Dice score of 0.942 on craniocerebral region segmentation, an average F1-score of 0.96 on classification and an average mean IOU of 0.497 on lesion localization.

Conclusion

We present computer-aided diagnosis algorithms for fetal brain ultrasound images based on deep convolutional neural networks. Our algorithms could be potentially applied in diagnosis assistance and are expected to help junior doctors in making clinical decision and reducing false negatives of fetal brain abnormalities.

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Funding

This study was funded by the National Natural Science Foundation of China (Grant Nos. 81571687 and 61771007), the Science and Technology Development Plan of Guangdong Province (Grant Nos. 2017A020214013 and 2020B010166002) and the Health and Medical Collaborative Innovation Project of Guangzhou City (Grant Number 201803010021).

Author information

Author notes

  1. Baihong Xie and Ting Lei have contributed equally to this work.

Authors and Affiliations

  1. South China University of Technology, Guangzhou, China

    Baihong Xie, Hongmin Cai & Jianbo Xian

  2. Department of Ultrasonic Medicine, Fetal Medical Center, First Affiliated Hospital of Sun Yat-sen University, Zhongshan Er Road 58, Guangzhou, 510080, Guangdong, China

    Ting Lei, Miao He, Lihe Zhang & Hongning Xie

  3. Guangzhou Aiyunji Information Technology Co., Ltd., Guangzhou, China

    Nan Wang & Jianbo Xian

Authors
  1. Baihong Xie
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  2. Ting Lei
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  4. Hongmin Cai
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Correspondence to Hongning Xie.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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The study protocol was approved by the Institutional Review Board of the First Affiliated Hospital of Sun Yat-sen University.

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Cite this article

Xie, B., Lei, T., Wang, N. et al. Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks. Int J CARS 15, 1303–1312 (2020). https://doi.org/10.1007/s11548-020-02182-3

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  • DOI: https://doi.org/10.1007/s11548-020-02182-3

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