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

Tao Peng, Caiyin Tang, Yiyun Wu, Jing Cai

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number878104
JournalFrontiers in Oncology
Volume12
DOIs
Publication statusPublished - 7 Jun 2022

Keywords

  • accurate prostate segmentation
  • constraint closed polygonal segment model
  • improved differential evolution-based method
  • interpretable mathematical model expression
  • machine learning
  • principal curve
  • transrectal ultrasound

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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