Combining imaging and shape features for prediction tasks of Alzheimer's disease classification and brain age regression
Journal:
arXiv
Published Date:
Jan 14, 2025
Abstract
We investigate combining imaging and shape features extracted from MRI for
the clinically relevant tasks of brain age prediction and Alzheimer's disease
classification. Our proposed model fuses ResNet-extracted image embeddings with
shape embeddings from a bespoke graph neural network. The shape embeddings are
derived from surface meshes of 15 brain structures, capturing detailed
geometric information. Combined with the appearance features from T1-weighted
images, we observe improvements in the prediction performance on both tasks,
with substantial gains for classification. We evaluate the model using public
datasets, including CamCAN, IXI, and OASIS3, demonstrating the effectiveness of
fusing imaging and shape features for brain analysis.
