Deep Learning Captures More Accurate Diffusion Fiber Orientations Distributions than Constrained Spherical Deconvolution.
Vishwesh NathKurt G. SchillingColin B. HansenPrasanna ParvathaneniAllison E. HainlineCamilo BermudezAndrew J. PlassardVaibhav A. JanveYurui GaoJustin A. BlaberIwona StepniewskaAdam W. AndersonBennett A. LandmanPublished in: CoRR (2019)
Keyphrases
- deep learning
- diffusion magnetic resonance imaging
- high angular resolution diffusion imaging
- spherical harmonics
- hardi data
- unsupervised learning
- unsupervised feature learning
- white matter
- machine learning
- single image
- denoising
- deep architectures
- mental models
- unit sphere
- weakly supervised
- diffusion tensor imaging
- diffusion tensor images
- orientation distribution function
- image processing
- tensor field
- anisotropic diffusion
- co occurrence
- supervised learning
- computer vision