DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification

TL;DR

DeepBrainPrint is a semi-self-supervised contrastive learning framework that creates robust brain MRI fingerprints for efficient patient re-identification across large datasets, handling variations in imaging conditions and disease progression.

Contribution

It introduces a novel contrastive deep learning approach combining self-supervised and supervised paradigms with specialized transformations for robust MRI retrieval.

Findings

Outperforms previous MRI re-identification methods.

Effective in handling intensity variations and disease progression.

Validated on large ADNI dataset and synthetic multimodal datasets.

Abstract

Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within these datasets (a process known as re-identification). To address this issue, we propose an AI-powered medical imaging retrieval framework called DeepBrainPrint, which is designed to retrieve brain MRI scans of the same patient. Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations. First, we use a combination of self-supervised and supervised paradigms to create an effective brain fingerprint from MRI scans that can be used for real-time image retrieval. Second, we use a special weighting function to guide the training and improve model convergence. Third, we introduce new imaging transformations to improve retrieval robustness in the presence of intensity variations (i.e. different scan contrasts), and to account for age and disease progression in patients. We tested DeepBrainPrint on a large dataset of T1-weighted brain MRIs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and on a synthetic dataset designed to evaluate retrieval performance with different image modalities. Our results show that DeepBrainPrint outperforms previous methods, including simple similarity metrics and more advanced contrastive deep learning frameworks.