Brain Segmentation
60 papers with code • 1 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Brain Segmentation
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Libraries
Use these libraries to find Brain Segmentation models and implementations
CrossMoDA
BRATS 2021
Tc1 Mouse cerebellum atlas
Multi-template MRI mouse brain atlas
Latest papers
Segment anything model (SAM) for brain extraction in fMRI studies
Brain extraction and removal of skull artifacts from magnetic resonance images (MRI) is an important preprocessing step in neuroimaging analysis.
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging
We present new metrics to validate the intra- and inter-subject robustness of Brain-ID features, and evaluate their performance on four downstream applications, covering contrast-independent (anatomy reconstruction/contrast synthesis, brain segmentation), and contrast-dependent (super-resolution, bias field estimation) tasks.
UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation
The different predictions in these duplicated heads are used to obtain pseudo labels for unlabeled target-domain images and their uncertainty to identify reliable pseudo labels.
Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentation
romainvala/synthetic_learning_on_dhcp
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We then evaluate the synthetic learning approach and confirm its robustness to variations in image contrast by reporting the capacity of such a model to segment both T1- and T2-weighted images from the same individuals.
Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration
masilab/unest
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Subsequently, the model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are available.
Two Independent Teachers are Better Role Model
AfifaKhaled/Two-Independent-Teachers-are-Better-Role-Model
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In this work, we address the above limitations by designing a new deep-learning model, called 3D-DenseUNet, which works as adaptable global aggregation blocks in down-sampling to solve the issue of spatial information loss.
Model-based inexact graph matching on top of CNNs for semantic scene understanding
On FASSEG data, results show that our module improves accuracy of the CNN by about 6. 3% (the Hausdorff distance decreases from 22. 11 to 20. 71).
Reliable brain morphometry from contrast‐enhanced T1w‐MRI in patients with multiple sclerosis
SCAN-NRAD/DL-DiReCT
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Human Brain Mapping 2022
The segmentations were derived with FreeSurfer from the non-enhanced image and used as ground truth for the coregistered CE image.
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
masilab/unest
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Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis.
Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
BBillot/SynthSeg
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Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset.
