Identifying Quantitative and Explanatory Tumor Indexes from Dynamic Contrast Enhanced Ultrasound

Peng Wan, Chunrui Liu, Fang Chen, Jing Qin, Daoqiang Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Contrast-enhanced ultrasound (CEUS) has been one of the most promising imaging techniques in tumor differential diagnosis since the real-time view of intra-tumor blood microcirculation. Existing studies primarily focus on extracting those discriminative imaging features whereas lack medical explanations. However, accurate quantitation of some clinical experience-driven indexes regarding intra-tumor vascularity, such as tumor infiltration and heterogeneity, still faces significant limitations. To tackle this problem, we present a novel scheme to identify quantitative and explanatory tumor indexes from dynamic CEUS sequences. Specifically, our method mainly comprises three steps: 1) extracting the stable pixel-level perfusion pattern from dynamic CEUS imaging using an improved stable principal component pursuit (SPCP) algorithm; 2) performing local perfusion variation comparison by the proposed Phase-constrained Wasserstein (PCW) distance; 3) estimating three clinical knowledge-induced tumor indexes, i.e. infiltration, regularity, and heterogeneity. The effectiveness of this method was evaluated on our collected CEUS dataset of thyroid nodules, and the resulting infiltration and heterogeneity index with p< 0.05 between different pathological types validated the efficacy of this quantitation scheme in thyroid nodule diagnosis.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
EditorsMarleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
PublisherSpringer Science and Business Media Deutschland GmbH
Pages638-647
Number of pages10
ISBN (Print)9783030872366
DOIs
Publication statusPublished - Sep 2021
Event24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 27 Sep 20211 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12908 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period27/09/211/10/21

Keywords

  • Contrast enhanced ultrasound
  • Perfusion analysis
  • Quantitative parameters estimation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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