Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE

@inproceedings{Volokitin2020ModellingTD,
  title={Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE},
  author={Anna Volokitin and E. Erdil and Neerav Karani and K. C. Tezcan and X. Chen and L. Gool and Ender Konukoglu},
  booktitle={MICCAI},
  year={2020}
}
  • Anna Volokitin, E. Erdil, +4 authors Ender Konukoglu
  • Published in MICCAI 2020
  • Computer Science, Engineering
  • Probabilistic modelling has been an essential tool in medical image analysis, especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep learning techniques for estimating highdimensional distributions, in particular Variational Autoencoders (VAEs), opened up new avenues for probabilistic modeling. Modelling of volumetric data has remained a challenge, however, because constraints on available computation and training data make it difficult effectively leverage VAEs, which are… CONTINUE READING
    Sampling possible reconstructions of undersampled acquisitions in MR imaging
    Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 21 REFERENCES
    V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
    • 2,082
    • PDF
    Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks
    • 12
    • Highly Influential
    • PDF
    U-Net: Convolutional Networks for Biomedical Image Segmentation
    • 15,499
    • PDF
    Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
    • 84
    • PDF
    MR Image Reconstruction Using Deep Density Priors
    • 33
    • PDF
    Automated segmentation of hippocampal subfields from ultra‐high resolution in vivo MRI
    • 319
    • PDF
    Improved Techniques for Training GANs
    • 3,585
    • PDF
    N4ITK: Improved N3 Bias Correction
    • 1,892
    FreeSurfer
    • 1,238
    • PDF
    Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
    • 47
    • PDF