Semi-Automatic Prostate Segmentation From Ultrasound Images Using Machine Learning and Principal Curve Based on Interpretable Mathematical Model Expression

Journal Article (Journal Article)

Accurate prostate segmentation in transrectal ultrasound (TRUS) is a challenging problem due to the low contrast of TRUS images and the presence of imaging artifacts such as speckle and shadow regions. To address this issue, we propose a semi-automatic model termed Hybrid Segmentation Model (H-SegMod) for prostate Region of Interest (ROI) segmentation in TRUS images. H-SegMod contains two cascaded stages. The first stage is to obtain the vertices sequences based on an improved principal curve-based model, where a few radiologist-selected seed points are used as prior. The second stage is to find a map function for describing the smooth prostate contour based on an improved machine learning model. Experimental results show that our proposed model achieved superior segmentation results compared with several other state-of-the-art models, achieving an average Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (Ω), and Accuracy (ACC) of 96.5%, 95.2%, and 96.3%, respectively.

Full Text

Duke Authors

Cited Authors

  • Peng, T; Tang, C; Wu, Y; Cai, J

Published Date

  • June 7, 2022

Published In

Volume / Issue

  • 12 /

Electronic International Standard Serial Number (EISSN)

  • 2234-943X

Digital Object Identifier (DOI)

  • 10.3389/fonc.2022.878104

Citation Source

  • Scopus