Prostate Segmentation of Ultrasound Images Based on Interpretable-Guided Mathematical Model

Tao Peng, Caiyin Tang, Jing Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Ultrasound prostate segmentation is challenging due to the low contrast of transrectal ultrasound (TRUS) images and the presence of imaging artifacts such as speckle and shadow regions. In this work, we propose an improved principal curve-based & differential evolution-based ultrasound prostate segmentation method (H-SegMod) based on an interpretable-guided mathematical model. Comparing with existing related studies, H-SegMod has three main merits and contributions: (1) The characteristic of the principal curve on automatically approaching the center of the dataset is utilized by our proposed H-SegMod. (2) When acquiring the data sequences, we use the principal curve-based constraint closed polygonal segment model, which uses different initialization, normalization, and vertex filtering methods. (3) We propose a mathematical map model (realized by differential evolution-based neural network) to describe the smooth prostate contour represented by the output of neural network (i.e., optimized vertices) so that it can match the ground truth contour. Compared with the traditional differential evolution method, we add different mutation steps and loop constraint conditions. Both quantitative and qualitative evaluation studies on a clinical prostate dataset show that our method achieves better segmentation than many state-of-the-art methods.

Original languageEnglish (US)
Title of host publicationMultiMedia Modeling - 28th International Conference, MMM 2022, Proceedings
EditorsBjörn Þór Jónsson, Cathal Gurrin, Minh-Triet Tran, Duc-Tien Dang-Nguyen, Anita Min-Chun Hu, Binh Huynh Thi Thanh, Benoit Huet
PublisherSpringer Science and Business Media Deutschland GmbH
Pages166-177
Number of pages12
ISBN (Print)9783030983574
DOIs
StatePublished - 2022
Event28th International Conference on MultiMedia Modeling, MMM 2022 - Phu Quoc, Viet Nam
Duration: Jun 6 2022Jun 10 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13141 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th International Conference on MultiMedia Modeling, MMM 2022
Country/TerritoryViet Nam
CityPhu Quoc
Period6/6/226/10/22

Keywords

  • Interpretable-guided mathematical model
  • Principal curve and neural network
  • Ultrasound prostate segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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