All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems.

Journal: Computer methods and programs in biomedicine
PMID: 38677080

Abstract

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) models trained on multi-centric and multi-device studies can provide more robust insights and research findings compared to single-center studies. However, variability in acquisition protocols and equipment can introduce inconsistencies that hamper the effective pooling of multi-source datasets. This systematic review evaluates strategies for image harmonization, which standardizes appearances to enable reliable AI analysis of multi-source medical imaging.

Authors

  • Silvia Seoni
    Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy.
  • Alen Shahini
    Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
  • Kristen M Meiburger
    Department of Electronics and Telecommunications, Politecnico di Torino, Italy.
  • Francesco Marzola
  • Giulia Rotunno
    Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
  • U Rajendra Acharya
    School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Darling Heights, Australia.
  • Filippo Molinari
    Department of Electronics and Telecommunications, Politecnico di Torino, Italy.
  • Massimo Salvi

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