Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods
University of California, Los Angeles
Indexed incrossrefpubmed
Abstract
Automatic whole-brain extraction from magnetic resonance images (MRI), also known as skull stripping, is a key component in most neuroimage pipelines. As the first element in the chain, its robustness is critical for the overall performance of the system. Many skull stripping methods have been proposed, but the problem is not considered to be completely solved yet. Many systems in the literature have good performance on certain datasets (mostly the datasets they were trained/tuned on), but fail to produce satisfactory results when the acquisition conditions or study populations are different. In this paper we introduce a robust, learning-based brain extraction system (ROBEX). The method combines a…
Citation impact
603
total citations
- FWCI
- 15.96
- Percentile
- 100%
- References
- 56
Citations per year
Authors
4Topics & keywords
Topics
Keywords
- Discriminative model
- Computer science
- Artificial intelligence
- Robustness (evolution)
- Generative model
- Segmentation
- Pattern recognition (psychology)
- Image segmentation
UN Sustainable Development Goals
- Reduced inequalities
No related works found for this paper.