Puglisi, Lemuel;
Eshaghi, Arman;
Parker, Geoff JM;
Barkhof, Frederik;
Alexander, Daniel C;
Ravì, Daniele;
(2023)
DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification.
In: Oguz, Ipek and Noble, Jack H and Li, Xiaoxiao and Styner, Martin and Baumgartner, Christian and Rusu, Mirabela and Heimann, Tobias and Kontos, Despina and Landman, Bennett A and Dawant, Benoit M, (eds.)
Proceedings of Machine Learning Research.
(pp. pp. 716-729).
PMLR
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification |
| Event: | Medical Imaging with Deep Learning |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.mlr.press/v227/puglisi24a.html |
| Language: | English |
| Additional information: | © 2023 CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/), L. Puglisi1 et al. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10188572 |
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