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 with no code
Region-based U-net for accelerated training and enhanced precision in deep brain segmentation
Segmentation of brain structures on MRI is the primary step for further quantitative analysis of brain diseases.
Transferring Ultrahigh-Field Representations for Intensity-Guided Brain Segmentation of Low-Field Magnetic Resonance Imaging
Specifically, our adaptive fusion module aggregates 7T-like features derived from the LF image by a pre-trained network and then refines them to be effectively assimilable UHF guidance into LF image features.
Deep Learning for Medical Image Segmentation with Imprecise Annotation
Medical image segmentation (MIS) plays an instrumental role in medical image analysis, where considerable efforts have been devoted to automating the process.
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
Artificial Intelligence in Fetal Resting-State Functional MRI Brain Segmentation: A Comparative Analysis of 3D UNet, VNet, and HighRes-Net Models
Aim: This study introduced a novel application of artificial intelligence (AI) for automated brain segmentation in fetal brain fMRI, magnetic resonance imaging (fMRI).
All Sizes Matter: Improving Volumetric Brain Segmentation on Small Lesions
Our experiments demonstrate the utility of the ad ditional blob loss and the subtraction sequence.
Brain MRI Segmentation using Template-Based Training and Visual Perception Augmentation
Deep learning models usually require sufficient training data to achieve high accuracy, but obtaining labeled data can be time-consuming and labor-intensive.
Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis
This paper investigates the numerical uncertainty of Convolutional Neural Networks (CNNs) inference for structural brain MRI analysis.
Direct segmentation of brain white matter tracts in diffusion MRI
The new methods can serve many critically important clinical and scientific applications that require accurate and reliable non-invasive segmentation of white matter tracts.
UM-CAM: Uncertainty-weighted Multi-resolution Class Activation Maps for Weakly-supervised Fetal Brain Segmentation
To this end, we propose a novel weakly-supervised method with image-level labels based on semantic features and context information exploration.
