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Radiomics in medical imaging—detection, extraction and segmentation

  • Jie Tian*
  • , Di Dong
  • , Zhenyu Liu
  • , Yali Zang
  • , Jingwei Wei
  • , Jiangdian Song
  • , Wei Mu
  • , Shuo Wang
  • , Mu Zhou
  • *Corresponding author for this work
  • CAS - Institute of Automation
  • Northeastern University China
  • Stanford University

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Radiomics, as a newly emerging technology, converts medical images into high-dimensional data via high-throughput extraction of quantitative features, followed by subsequent data analysis for decision support. It identifies general diagnostic or prognostic phenotypes with target clinical need, providing an unprecedented opportunity to improve individualized treatment in cancer at low cost. In this chapter, we will introduce radiomics from its development to its clinical applications. We divide the clinical applications into three sections based on three most common medical modality, including computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET), to give a comprehensive introduction of how radiomics works with the example of a typical cancer type. The workflow and detailed technology skills are well described in each section.

Original languageEnglish
Title of host publicationIntelligent Systems Reference Library
PublisherSpringer Science and Business Media Deutschland GmbH
Pages267-333
Number of pages67
DOIs
StatePublished - 2018
Externally publishedYes

Publication series

NameIntelligent Systems Reference Library
Volume140
ISSN (Print)1868-4394
ISSN (Electronic)1868-4408

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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