Skull Stripping
12 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Skull Stripping
Most implemented papers
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.
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.
Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network
The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans.
Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network
The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans.
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.
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.
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.
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).
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.
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.
