  To build a robust and practical content-based image retrieval (CBIR) system
that is applicable to a clinical brain MRI database, we propose a new framework
-- Disease-oriented image embedding with pseudo-scanner standardization
(DI-PSS) -- that consists of two core techniques, data harmonization and a
dimension reduction algorithm. Our DI-PSS uses skull stripping and
CycleGAN-based image transformations that map to a standard brain followed by
transformation into a brain image taken with a given reference scanner. Then,
our 3D convolutioinal autoencoders (3D-CAE) with deep metric learning acquires
a low-dimensional embedding that better reflects the characteristics of the
disease. The effectiveness of our proposed framework was tested on the
T1-weighted MRIs selected from the Alzheimer's Disease Neuroimaging Initiative
and the Parkinson's Progression Markers Initiative. We confirmed that our PSS
greatly reduced the variability of low-dimensional embeddings caused by
different scanner and datasets. Compared with the baseline condition, our PSS
reduced the variability in the distance from Alzheimer's disease (AD) to
clinically normal (CN) and Parkinson disease (PD) cases by 15.8-22.6% and
18.0-29.9%, respectively. These properties allow DI-PSS to generate lower
dimensional representations that are more amenable to disease classification.
In AD and CN classification experiments based on spectral clustering, PSS
improved the average accuracy and macro-F1 by 6.2% and 10.7%, respectively.
Given the potential of the DI-PSS for harmonizing images scanned by MRI
scanners that were not used to scan the training data, we expect that the
DI-PSS is suitable for application to a large number of legacy MRIs scanned in
heterogeneous environments.
