Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets.

Area of Science:

• Neuroimaging and Artificial Intelligence
• Medical Image Analysis
• Machine Learning in Healthcare

Background:

• Large, multi-site brain imaging datasets are essential for training advanced deep learning (DL) tools for structural magnetic resonance (MR) imaging.
• Aggregating smaller datasets introduces variability, known as batch effects, stemming from diverse acquisition and processing protocols.
• Understanding and managing batch effects is critical to ensure DL tool generalizability and avoid performance degradation.

Purpose of the Study:

• To review the impact of batch effects in deep learning applications for structural brain MR imaging.
• To explore methods for addressing batch effects, including data harmonization and domain adaptation.
• To discuss the advantages, disadvantages, and challenges of these approaches in multi-site neuroimaging datasets.

Main Methods:

• Narrative review of deep learning applications in structural brain MR imaging.
• Examination of datasets aggregated from multiple sites with varying acquisition protocols.
• Analysis of two main approaches for managing batch effects: explicit data harmonization and implicit domain adaptation.

Main Results:

• Batch effects can arise from equipment, imaging techniques, and processing, influencing DL tool performance both positively and negatively.
• Data harmonization aims to explicitly minimize unwanted batch effects through standardization and quality control.
• Domain adaptation implicitly handles batch effects by developing DL tools robust to variations.

Conclusions:

• Both data harmonization and domain adaptation offer distinct strategies for managing batch effects in brain imaging DL.
• Careful consideration of batch effect impacts is necessary to ensure DL tool outputs reflect true pathology, not data artifacts.
• Future research should address key challenges in developing robust and generalizable DL tools for heterogeneous neuroimaging data.