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We propose a novel machine learning strategy for studying neuroanatomicalshape variation. Our model works with volumetric binary segmentation images,and requires no pre-processing such as the extraction of surface points or amesh. The learned shape descriptor is invariant to affine transformations,including shifts, rotations and scaling. Thanks to the adopted autoencoderframework, inter-subject differences are automatically enhanced in the learnedrepresentation, while intra-subject variances are minimized. Our experimentalresults on a shape retrieval task showed that the proposed representationoutperforms a state-of-the-art benchmark for brain structures extracted fromMRI scans.

A Convolutional Autoencoder Approach to Learn Volumetric Shape Representations for Brain Structures