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An Accurate Segmentation Framework for Static Ultrasound Images of the Gestational Sac

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Abstract

Purpose

Abnormal morphology characteristics of the gestational sac give accurate prediction for the early spontaneous abortion. However, due to the high noise and the weak transmission of ultrasonic signals, accurate segmentation is an urgent and challenging task for quantitative morphology analysis of gestational sacs in ultrasound images.

Methods

This paper proposes an accurate segmentation framework for gestational sac ultrasound images from two different scanning machines based on an improved level-set algorithm driven by shape domain-specific knowledge. Firstly, the gestational sac candidate image is roughly segmented using the Chan-Vese (CV) model, and the minimum convex polygon is reserved according to the quasi-circular character of the sacs. Then, the gestational sacs are divided into two categories, such as the concave and convex ones, followed by separate corresponding processing for further accurate segmentation.

Results

A total of 194 ultrasound images of the gestational sacs of 6–9 were processed in this experiment. For testing, we have obtained the mean Dice and Intersection over Union (IOU) value of 0.916 and 0.842, and the average sensitivity (SEN) and positive prediction rate (PPV) are 0.958 and 0.890, respectively. The evaluation results show that our proposed segmentation framework has better performance than other commonly used segmentation methods.

Conclusion

The evaluation results show that our proposed segmentation framework has better performance than other commonly used segmentation methods.

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Funding

This work was supported by the National Key Research and Development Program (2016YFC1000505), the National Natural Science Foundation of China (Grant Numbers: 81871219, 81671469), and the Liao Ning Revitalization Talents Program (XLYC1902099).

Author information

Authors and Affiliations

  1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110819, Liaoning, China

    Chenghuan Yin & Qixin Zhang

  2. Key Laboratory of Health Ministry for Congenital Malformation, Shengjing Hospital, China Medical University, Shenyang, China

    Yu Wang & Zhengwei Yuan

  3. College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, Guangdong, China

    Fangfang Han

  4. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Yudong Yao

Authors
  1. Chenghuan Yin
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  2. Yu Wang
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  3. Qixin Zhang
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  4. Fangfang Han
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  5. Zhengwei Yuan
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Correspondence to Fangfang Han or Yudong Yao.

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The research related to human subject use has complied with all the relevant national regulations, and institutional policies. (The hospital provides the proof).

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Cite this article

Yin, C., Wang, Y., Zhang, Q. et al. An Accurate Segmentation Framework for Static Ultrasound Images of the Gestational Sac. J. Med. Biol. Eng. 42, 49–62 (2022). https://doi.org/10.1007/s40846-021-00674-4

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  • DOI: https://doi.org/10.1007/s40846-021-00674-4

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