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review-article

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

Authors:

Kristen M. Meiburger,

Francesco Marzola,

Giulia Rotunno,

U. Rajendra Acharya,

Filippo Molinari,

Massimo SalviAuthors Info & Claims

Volume 250, Issue C

https://doi.org/10.1016/j.cmpb.2024.108200

Published: 01 June 2024 Publication History

Highlights

•

Summary of image harmonization techniques in multi-centric healthcare data analysis.

•

Evaluation of commonly used data harmonization strategies across modalities.

•

Several imge modalities covered including radiology, pathology, and fluorescence.

•

Most adopted approaches are grayscale/color normalization and resampling.

•

Harmonization ensures consistency and improves reliability of pooled data analysis.

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.

Methods

A literature search using PRISMA guidelines was conducted to identify relevant papers published between 2013 and 2023 analyzing multi-centric and multi-device medical imaging studies that utilized image harmonization approaches.

Results

Common image harmonization techniques included grayscale normalization (improving classification accuracy by up to 24.42 %), resampling (increasing the percentage of robust radiomics features from 59.5 % to 89.25 %), and color normalization (enhancing AUC by up to 0.25 in external test sets). Initially, mathematical and statistical methods dominated, but machine and deep learning adoption has risen recently. Color imaging modalities like digital pathology and dermatology have remained prominent application areas, though harmonization efforts have expanded to diverse fields including radiology, nuclear medicine, and ultrasound imaging. In all the modalities covered by this review, image harmonization improved AI performance, with increasing of up to 24.42 % in classification accuracy and 47 % in segmentation Dice scores.

Conclusions

Continued progress in image harmonization represents a promising strategy for advancing healthcare by enabling large-scale, reliable analysis of integrated multi-source datasets using AI. Standardizing imaging data across clinical settings can help realize personalized, evidence-based care supported by data-driven technologies while mitigating biases associated with specific populations or acquisition protocols.

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Cited By

Stefano A(2024)Challenges and limitations in applying radiomics to PET imagingComputers in Biology and Medicine10.1016/j.compbiomed.2024.108827179:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108827
Liapi GLoizou CPattichis CPattichis MNicolaides AGriffin MKyriacou E(2024)Assessing the impact of ultrasound image standardization in deep learning-based segmentation of carotid plaque typesComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2024.108460257:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.cmpb.2024.108460

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cover image Computer Methods and Programs in Biomedicine

Computer Methods and Programs in Biomedicine Volume 250, Issue C

Jun 2024

412 pages

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Published: 01 June 2024

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Stefano A(2024)Challenges and limitations in applying radiomics to PET imagingComputers in Biology and Medicine10.1016/j.compbiomed.2024.108827179:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108827
Liapi GLoizou CPattichis CPattichis MNicolaides AGriffin MKyriacou E(2024)Assessing the impact of ultrasound image standardization in deep learning-based segmentation of carotid plaque typesComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2024.108460257:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.cmpb.2024.108460

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