Upgrade to Pro — share decks privately, control downloads, hide ads and more …

DL in MRI

Tsung-Yung Lu
October 29, 2019
81

DL in MRI

Application of deep learning in MRI

Tsung-Yung Lu

October 29, 2019
Tweet

Transcript

  1. Machine Learning ► Artificial neural networks ► Deep learning ►

    Convolutional neural networks ► Generative adversarial network 4
  2. 9

  3. Image reconstruction Visualization results of intermediate steps during the iterations

    of a reconstruction. (a) Undersampled image by acceleration factor 9 (b) Ground Truth (c-l) Results from intermediate steps 1 to 10 in a reconstruction process CRNN Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
  4. Image reconstruction • The comparison of reconstructions on spatial dimension

    with their error maps. • (a) Ground Truth • (b) Undersampled image by acceleration factor 9 • (c,d) Proposed-B • (e,f) 3D CNN • (g,h) 3D CNN-S • (i,j) k-t FOCUSS • (k,l) k-t SLR
  5. Image restoration : Denoising 14 Deep Learning Approaches for Detection

    and Removal of Ghosting Artifacts in MR Spectroscopy
  6. Image SR ► Super Resolution GAN (SRGAN) ► Network ►

    Discriminator : HR image (T/F) ► Generator : LR→HR 15
  7. Image synthesis : DCGAN • Data augmentation • Network •

    Discriminator : Real image • Generator : Synthetic image
  8. SLANT : Whole Brain Segmentation 21 3D Whole Brain Segmentation

    using Spatially Localized Atlas Network Tiles
  9. Automated Segmentation of Polycystic Kidneys 23 Performance of an Artificial

    Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5537093/
  10. Brain ► Brain extraction ► Functional connectomes ► Structural connectomes

    ► Brain age ► Alzheimer’s disease ► Vascular lesions ► Identification of MRI contrast ► Meningioma ► Glioma ► Multiple sclerosis 25
  11. Challenges ► Medical imaging data sets and repositories ► Medical

    imaging competitions ► Data ► Interpretability 32
  12. Data Sets and Repositories ► [TCIA] ► “Large” ► cancer

    imaging ► [OpenNeuro] ► brain images ► 168 studies ► 4,718 participants ► [UK Biobank] ► 15,000 participants ► [ADNI] ► Alzheimer’s disease neuroimaging ► 2,000 participants 33
  13. REFERENCE ► Alexander SelvikvågLundervold, al. An overview of deep learning

    in medical imaging focusing on MRI, Zeitschrift für Medizinische Physik Volume 29, Issue 2, May 2019, Pages 102-127. 35