Skull Stripping
12 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Latest papers
Neural Pre-Processing: A Learning Framework for End-to-end Brain MRI Pre-processing
novestars/neural-pre-processing
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Head MRI pre-processing involves converting raw images to an intensity-normalized, skull-stripped brain in a standard coordinate space.
Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?
Our experiments show that the choice of a BE method can compromise up to 15. 7% of the tumor segmentation performance.
Negligible effect of brain MRI data preprocessing for tumor segmentation
medimair/brain-mri-processing-pipeline
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Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability.
Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks
jmlipman/MedicDeepLabv3Plus
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We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with lesions.
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast
BBillot/SynthSR
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Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well - typically requiring T1 scans (e. g., MP-RAGE).
A survey of loss functions for semantic segmentation
shruti-jadon/Semantic-Segmentation-Loss-Functions
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In this paper, we have summarized some of the well-known loss functions widely used for Image Segmentation and listed out the cases where their usage can help in fast and better convergence of a model.
ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation
ymli39/ACEnet-for-Neuroanatomy-Segmentation
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However, existing 2D deep learning methods are not equipped to effectively capture 3D spatial contextual information that is needed to achieve accurate brain structure segmentation.
Training of a Skull-Stripping Neural Network with efficient data augmentation
Skull-stripping methods aim to remove the non-brain tissue from acquisition of brain scans in magnetic resonance (MR) imaging.
Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Consensus-based Silver standard Masks
Our use of silver standard masks reduced the cost of manual annotation, decreased inter-intra-rater variability, and avoided CNN segmentation super-specialization towards one specific manual annotation guideline that can occur when gold standard masks are used.
CompNet: Complementary Segmentation Network for Brain MRI Extraction
raun1/Complementary_Segmentation_Network
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Brain extraction is a fundamental step for most brain imaging studies.
