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
Most implemented papers
Recalibrating 3D ConvNets with Project & Excite
ai-med/squeeze_and_excitation
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Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging.
ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging
Medical image segmentation is one of the major challenges addressed by machine learning methods.
MixNet: Multi-modality Mix Network for Brain Segmentation
looooongChen/MRBrainS-Brain-Segmentation
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Automated brain structure segmentation is important to many clinical quantitative analysis and diagnoses.
A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients.
Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices
HiLab-git/UGIR
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Experimental results show that: (1) our proposed CNN obtains uncertainty estimation in real time which correlates well with mis-segmentations, (2) the proposed interactive level set is effective and efficient for refinement, (3) UGIR obtains accurate refinement results with around 30% improvement of efficiency by using uncertainty to guide user interactions.
A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis.
Local Temperature Scaling for Probability Calibration
uncbiag/LTS
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ICCV 2021
Thus, we propose a learning-based calibration method that focuses on multi-label semantic segmentation.
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels
Segmentation of the developing fetal brain is an important step in quantitative analyses.
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders.
Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation
DL+DiReCT is a promising combination of a deep learning‐based method with a traditional registration technique to detect subtle changes in cortical thickness.
