Deep learning for the harmonization of structural MRI scans: a survey
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Summary
This summary is machine-generated.This review explores deep learning for medical image harmonization, addressing data inconsistencies from varied sources. It guides researchers in selecting methods to improve data compatibility for advanced medical image processing.
Area Of Science
- Medical Imaging
- Artificial Intelligence
- Data Science
Background
- Medical imaging datasets often exhibit inconsistencies due to diverse scanners, protocols, and settings across institutions.
- These variations compromise data consistency and compatibility, hindering reliable medical image processing and analysis.
- Image harmonization is essential to mitigate these effects and ensure standardized data for research.
Purpose Of The Study
- To review and analyze the latest deep learning techniques for medical image harmonization.
- To evaluate the strengths and limitations of various deep learning architectures and learning algorithms.
- To provide guidance for researchers in selecting appropriate harmonization methods.
Main Methods
- Comprehensive overview of fundamental image harmonization strategies, datasets, metrics, and scanner characteristics.
- Analysis of deep learning harmonization techniques based on network architecture (U-Net, GANs, VAEs, transformers, etc.).
- Investigation of Disentangled Representation Learning (DRL) as a key learning algorithm.
Main Results
- Deep learning approaches, particularly GANs and VAEs, show significant advancements in medical image harmonization.
- Different network architectures and learning strategies offer varying performance and suitability for specific harmonization tasks.
- A key limitation identified is the lack of standardized quantitative comparisons across existing methods.
Conclusions
- Deep learning offers powerful tools for harmonizing medical imaging data, improving consistency and compatibility.
- Selecting the right architecture and learning algorithm is crucial for effective harmonization.
- Further research is needed to establish comprehensive evaluation metrics and address current limitations.
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