Ultrasound Prostate Segmentation Using Adaptive Selection Principal Curve and Smooth Mathematical Model

Tao Peng, Yiyun Wu, Jing Zhao, Caishan Wang, Jin Wang, Jing Cai

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Accurate prostate segmentation in ultrasound images is crucial for the clinical diagnosis of prostate cancer and for performing image-guided prostate surgery. However, it is challenging to accurately segment the prostate in ultrasound images due to their low signal-to-noise ratio, the low contrast between the prostate and neighboring tissues, and the diffuse or invisible boundaries of the prostate. In this paper, we develop a novel hybrid method for segmentation of the prostate in ultrasound images that generates accurate contours of the prostate from a range of datasets. Our method involves three key steps: (1) application of a principal curve-based method to obtain a data sequence comprising data coordinates and their corresponding projection index; (2) use of the projection index as training input for a fractional-order-based neural network that increases the accuracy of results; and (3) generation of a smooth mathematical map (expressed via the parameters of the neural network) that affords a smooth prostate boundary, which represents the output of the neural network (i.e., optimized vertices) and matches the ground truth contour. Experimental evaluation of our method and several other state-of-the-art segmentation methods on datasets of prostate ultrasound images generated at multiple institutions demonstrated that our method exhibited the best capability. Furthermore, our method is robust as it can be applied to segment prostate ultrasound images obtained at multiple institutions based on various evaluation metrics.

Original languageEnglish
Pages (from-to)947-963
Number of pages17
JournalJournal of Digital Imaging
Volume36
Issue number3
DOIs
Publication statusPublished - Jun 2023

Keywords

  • Fractional-order-based neural network
  • Mean shift clustering
  • Principal curve
  • Smooth mathematical model
  • Ultrasound prostate segmentation

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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