Stochastic Cortical Self-Reconstruction
Christian Wachinger Dennis Hedderich Fabian Bongratz
Magnetic resonance imaging (MRI) is critical for diagnosing neurodegenerative
diseases, yet accurately assessing mild cortical atrophy remains a challenge
due to its subtlety. Automated cortex reconstruction, paired with healthy
reference ranges, aids in pinpointing pathological atrophy, yet their
generalization is limited by biases from image acquisition and processing. We
introduce the concept of stochastic cortical self-reconstruction (SCSR) that
creates a subject-specific healthy reference by taking MRI-derived thicknesses
as input and, therefore, implicitly accounting for potential confounders. SCSR
randomly corrupts parts of the cortex and self-reconstructs them from the
remaining information. Trained exclusively on healthy individuals, repeated
self-reconstruction generates a stochastic reference cortex for assessing
deviations from the norm. We present three implementations of this concept:
XGBoost applied on parcels, and two autoencoders on vertex level -- one based
on a multilayer perceptron and the other using a spherical U-Net. These models
were trained on healthy subjects from the UK Biobank and subsequently evaluated
across four public Alzheimer's datasets. Finally, we deploy the model on
clinical in-house data, where deviation maps' high spatial resolution aids in
discriminating between four types of dementia.