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H-ProMed: Ultrasound image segmentation based on the evolutionary neural network and an improved principal curve

Publication ,  Journal Article
Peng, T; Zhao, J; Gu, Y; Wang, C; Wu, Y; Cheng, X; Cai, J
Published in: Pattern Recognition
November 1, 2022

The purpose of this work is to develop a method for accurate and robust prostate segmentation in transrectal ultrasound (TRUS) images. These images are difficult to segment due to missing/ambiguous boundary between the prostate and neighboring structures, the presence of shadow artifacts, as well as the large variability in prostate shapes. This paper develops a novel hybrid method for TRUS prostate segmentation by combining an improved principal curve-based method with an evolutionary neural network; the former for achieving the data sequences while and the latter for improving the smoothness of the prostate contour. Both qualitative and quantitative experimental results showed that our proposed method achieved superior segmentation accuracy and robustness as compared to state-of-the-art methods. The average Dice similarity coefficient (DSC), Jaccard similarity coefficient (Ω), and accuracy (ACC) of prostate contours against ground-truths were 96.8%, 95.7%, and 96.4%, and the DSC of around 92% and 95% for other deep learning and hybrid methods, respectively.

Duke Scholars

Published In

Pattern Recognition

DOI

ISSN

0031-3203

Publication Date

November 1, 2022

Volume

131

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4605 Data management and data science
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing
 

Citation

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Peng, T., Zhao, J., Gu, Y., Wang, C., Wu, Y., Cheng, X., & Cai, J. (2022). H-ProMed: Ultrasound image segmentation based on the evolutionary neural network and an improved principal curve. Pattern Recognition, 131. https://doi.org/10.1016/j.patcog.2022.108890
Peng, T., J. Zhao, Y. Gu, C. Wang, Y. Wu, X. Cheng, and J. Cai. “H-ProMed: Ultrasound image segmentation based on the evolutionary neural network and an improved principal curve.” Pattern Recognition 131 (November 1, 2022). https://doi.org/10.1016/j.patcog.2022.108890.
Peng T, Zhao J, Gu Y, Wang C, Wu Y, Cheng X, et al. H-ProMed: Ultrasound image segmentation based on the evolutionary neural network and an improved principal curve. Pattern Recognition. 2022 Nov 1;131.
Peng, T., et al. “H-ProMed: Ultrasound image segmentation based on the evolutionary neural network and an improved principal curve.” Pattern Recognition, vol. 131, Nov. 2022. Scopus, doi:10.1016/j.patcog.2022.108890.
Peng T, Zhao J, Gu Y, Wang C, Wu Y, Cheng X, Cai J. H-ProMed: Ultrasound image segmentation based on the evolutionary neural network and an improved principal curve. Pattern Recognition. 2022 Nov 1;131.
Journal cover image

Published In

Pattern Recognition

DOI

ISSN

0031-3203

Publication Date

November 1, 2022

Volume

131

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4605 Data management and data science
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing