A Convolutional Autoencoder Approach To Learn Volumetric Shape Representations For Brain Structures

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 transformation...

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Bibliographic Details
Published inProceedings (International Symposium on Biomedical Imaging) Vol. 2019; pp. 1559 - 1562
Main Authors Yu, Evan M., Sabuncu, Mert R.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.04.2019
Subjects
Benchmark testing
Brain
Machine learning
Magnetic resonance imaging
Shape
Three-dimensional displays
Training
spatial transformers
deep learning
shape analysis
autoencoder
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Summary: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.
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ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2019.8759231