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

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Authors Evan M. Yu, Mert R. Sabuncu
Journal/Conference Name Proceedings - International Symposium on Biomedical Imaging
Paper Category
Paper Abstract We propose a novel machine learning strategy for studying neuroanatomical shape variation. Our model works with volumetric binary segmentation images, and requires no pre-processing such as the extraction of surface points or a mesh. The learned shape descriptor is invariant to affine transformations, including shifts, rotations and scaling. Thanks to the adopted autoencoder framework, inter-subject differences are automatically enhanced in the learned representation, while intra-subject variances are minimized. Our experimental results on a shape retrieval task showed that the proposed representation outperforms a state-of-the-art benchmark for brain structures extracted from MRI scans.
Date of publication 2018
Code Programming Language Python
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