Infant Brain Mri Segmentation
4 papers with code • 1 benchmarks • 0 datasets
Latest papers with no code
Infant brain MRI segmentation with dilated convolution pyramid downsampling and self-attention
In this paper, we propose a dual aggregation network to adaptively aggregate different information in infant brain MRI segmentation.
Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation
The proposed network, called 3D-SkipDenseSeg, exploits the advantage of the recently DenseNet for classification task and extends this to segment the 6-month infant brain tissue segmentation of magnetic resonance imaging (MRI).
InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation
We present a novel, parameter-efficient and practical fully convolutional neural network architecture, termed InfiNet, aimed at voxel-wise semantic segmentation of infant brain MRI images at iso-intense stage, which can be easily extended for other segmentation tasks involving multi-modalities.
Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets: Application to IsoIntense Infant Brain MRI Segmentation
Using our proposed training strategy based on similarity loss functions and patch prediction fusion we decrease the number of parameters in the network, reduce the complexity of the training process focusing the attention on less number of tasks, while mitigating the effects of data imbalance between labels and inaccuracies near patch borders.
Multi-stream 3D FCN with Multi-scale Deep Supervision for Multi-modality Isointense Infant Brain MR Image Segmentation
We present a method to address the challenging problem of segmentation of multi-modality isointense infant brain MR images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF).
Isointense infant brain MRI segmentation with a dilated convolutional neural network
Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter.