EDBT 2026 Demo / reviewers in the wild / expert
Yaniv Assaf
dblp:02/449
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2005
0000-0001-6828-7595ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% | |
| Computer graphics and multimedia
1 paper |
Image and video processing · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Medical and health informatics › neuroimaging
diffusion MRI |
0.1 | 1 | 2005 | Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI · NIPS 2005 |
Medical and health informatics › medical imaging
medical image analysis |
0.1 | 1 | 2005 | Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI · NIPS 2005 |
Image and video processing › image restoration
artifact removal |
0.0 | 1 | 2005 | Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI · NIPS 2005 |
Image and video processing
image restoration |
0.0 | 1 | 2005 | Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI · NIPS 2005 |
Methods — techniques the papers use, named apart from their topics
variational regularization · 0.1multiple tensor variational framework · 0.1diffusion tensor model · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRIabstractDiffusion Tensor Magnetic Resonance Imaging (DT-MRI) is a non inva- sive method for brain neuronal fibers delineation. Here we show a mod- ification for DT-MRI that allows delineation of neuronal fibers which are infiltrated by edema. We use the Muliple Tensor Variational (MTV) framework which replaces the diffusion model of DT-MRI with a mul- tiple component model and fits it to the signal attenuation with a vari- ational regularization mechanism. In order to reduce free water con- tamination we estimate the free water compartment volume fraction in each voxel, remove it, and then calculate the anisotropy of the remaining compartment. The variational framework was applied on data collected with conventional clinical parameters, containing only six diffusion di- rections. By using the variational framework we were able to overcome the highly ill posed fitting. The results show that we were able to find fibers that were not found by DT-MRI. Ofer Pasternak, Nir A. Sochen, Nathan Intrator, Yaniv Assaf |
NIPS | 4 |