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A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets

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Abstract

Detecting the organ boundary in an ultrasound image is challenging because of the poor contrast of ultrasound images and the existence of imaging artifacts. In this study, we developed a coarse-to-refinement architecture for multi-organ ultrasound segmentation. First, we integrated the principal curve–based projection stage into an improved neutrosophic mean shift–based algorithm to acquire the data sequence, for which we utilized a limited amount of prior seed point information as the approximate initialization. Second, a distribution-based evolution technique was designed to aid in the identification of a suitable learning network. Then, utilizing the data sequence as the input of the learning network, we achieved the optimal learning network after learning network training. Finally, a scaled exponential linear unit–based interpretable mathematical model of the organ boundary was expressed via the parameters of a fraction-based learning network. The experimental outcomes indicated that our algorithm 1) achieved more satisfactory segmentation outcomes than state-of-the-art algorithms, with a Dice score coefficient value of 96.68 ± 2.2%, a Jaccard index value of 95.65 ± 2.16%, and an accuracy of 96.54 ± 1.82% and 2) discovered missing or blurry areas.

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Data will be made available on reasonable request.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. School of Future Science and Engineering, Soochow University, Suzhou, China

    Tao Peng & Yidong Gu

  2. Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China

    Tao Peng & Jing Cai

  3. Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA

    Tao Peng

  4. Department of Medical Ultrasound, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, Jiangsu, China

    Yidong Gu

  5. Department of Radiology, The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, Jiangsu Province, China

    Ji Zhang

  6. Department of Ultrasonography, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China

    Yan Dong

  7. Department of Ultrasonic, The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, Jiangsu Province, China

    Gongye DI

  8. Department of Radio-Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, Jiangsu, China

    Wenjie Wang

  9. Department of Ultrasound, Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital, Beijing, China

    Jing Zhao

Authors
  1. Tao Peng
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  2. Yidong Gu
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  3. Ji Zhang
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  4. Yan Dong
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  5. Gongye DI
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  6. Wenjie Wang
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  7. Jing Zhao
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  8. Jing Cai
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Contributions

Tao Peng: Methodology, Coding, Writing—original draft. Yidong Gu: Data preprocessing, Analysis. Ji Zhang: Data preprocessing, Analysis. Yan Dong: Data preprocessing, Analysis. Gongye DI: Data preprocessing, Analysis. Wenjie Wang: Data preprocessing, Analysis. Jing Zhao: Data preprocessing, Analysis. Jing Cai: Supervision, Writing—review & editing.

Corresponding authors

Correspondence to Tao Peng or Jing Cai.

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Ethics Approval

This study involves a retrospective use of patients’ standard of care images, where the clinicians have obtained patients’ agreement before the ultrasound examination, which is an item covered by the medical insurance program.

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Peng, T., Gu, Y., Zhang, J. et al. A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets. J Digit Imaging 36, 1515–1532 (2023). https://doi.org/10.1007/s10278-023-00839-4

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