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Automatic fetal ultrasound image segmentation of first trimester for measuring biometric parameters based on deep learning

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Abstract

Transvaginal ultrasonography (TVS) is a common method used by doctors to monitor the embryonic development. In the early stage of pregnancy, doctors assess the growth and development of the embryo by measuring biological indicators such as gestational sac area (GSA), yolk sac diameter (YSD), and crown-rump length (CRL) in TVS images. Even though these indicators can be manually obtained by experienced physicians, the manual measurement process is time-consuming, inefficient, and heavily dependent on the sonographer's expertise. To improve this situation, we, here, aimed to establish a modified Unet model, namely AFG-net, which is capable of automatically obtaining the related clinical values required for measuring embryonic development. Using this method, the essential values, including gestational sac (GS), yolk sac (YS) and embryo region in the TVS image, were easily and accurately identified and located, which were further completely separated by image segmentation to obtain the corresponding measurement values. Notably, this model is able to achieve superior segmentation effect even when the input image with poor quality, low contrast, fuzzy region boundary and complex anatomical shape by applying some advanced methods such as attention fusion and guide filter. Consequently, our results showed our model demonstrated a higher average precision, Intersection Over Union (IOU), and Dice coefficient (Dice) of GS, YS and embryo compared to a normal Unet, with 94.75%, 86.15% and 92.11% versus 92.01%, 83.00%, and 90.00%, respectively. The absolute error between the biological indicators (GSA, YSD and CRL) automatically extracted from the segmentation results and the manual measurement results is 0.66mm. The automatic segmentation and measurement process significantly reduces the subjectivity of manual measurement and reduces the clinician workload. It also helps to improve diagnostic accuracy, enables repeatability and standardization in clinical practice, and provides a valuable tool for prenatal care and monitoring.

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Data availability

The data that support the findings of this study are available from Reproductive Center and the Imaging Department of the Reproductive and Genetic Hospital of CITIC-Xiangya but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Reproductive Center and the Imaging Department of the Reproductive and Genetic Hospital of CITIC-Xiangya.

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Acknowledgements

The authors gratefully acknowledge the financial support provided by Hunan Provincial Natural Science Foundation of China (2023JJ60491) and the Open Project of Xiangjiang Laboratory (22XJ02005).

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Authors and Affiliations

  1. School of Automation, Central South University, Changsha, China, 410083

    Lijue Liu & Duo Tang

  2. Xiangjiang Laboratory, Changsha, China, 410205

    Lijue Liu

  3. Hunan ZIXING Intelligent Medical Technology Co., Ltd, Changsha, China, 410221

    Lijue Liu

  4. Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, China, 410078

    Xihong Li & Yan Ouyang

  5. Clinical Research Center For Reproduction and Genetics in Hunan Province, Changsha, China, 410078

    Xihong Li & Yan Ouyang

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  1. Lijue Liu
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Contributions

Conceptualization: L.L., D.T. and X.L.; methodology: L.L., D.T., X.L. and Y.O.; software: D.T.; resources: L.L., D.T. and X.L.; data curation: X.L. and Y.O.; data analysis and interpretation: L.L., D.T., X.L., and Y.O.; writing—original draft preparation: L.L. and D.T.; writing—review and editing: L.L., D.T. and X.L.; project administration: L.L. and Y.O. The first four authors contributed equally to the paper. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Xihong Li.

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Liu, L., Tang, D., Li, X. et al. Automatic fetal ultrasound image segmentation of first trimester for measuring biometric parameters based on deep learning. Multimed Tools Appl 83, 27283–27304 (2024). https://doi.org/10.1007/s11042-023-16565-6

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