Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

• URL: http://arxiv.org/abs/2002.05692v1
• Date: Thu, 13 Feb 2020 18:18:51 GMT
• Title: Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE
• Authors: Petru-Daniel Tudosiu and Thomas Varsavsky and Richard Shaw and Mark Graham and Parashkev Nachev and Sebastien Ourselin and Carole H. Sudre and M. Jorge Cardoso
• Abstract summary: We show a VQ-VAE inspired network can efficiently encode a full-resolution 3D brain volume, compressing the data to $0.825%$ of the original size while maintaining image fidelity. We then demonstrate that VQ-VAE decoded images preserve the morphological characteristics of the original data through voxel-based morphology and segmentation experiments.
• Score: 4.221619479687068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical volumetric data at higher resolutions. Recently, Vector-Quantised Variational Autoencoders (VQ-VAE) have been proposed as an efficient generative unsupervised learning approach that can encode images to a small percentage of their initial size, while preserving their decoded fidelity. Here, we show a VQ-VAE inspired network can efficiently encode a full-resolution 3D brain volume, compressing the data to $0.825\%$ of the original size while maintaining image fidelity, and significantly outperforming the previous state-of-the-art. We then demonstrate that VQ-VAE decoded images preserve the morphological characteristics of the original data through voxel-based morphology and segmentation experiments. Lastly, we show that such models can be pre-trained and then fine-tuned on different datasets without the introduction of bias.

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