Brain Image Segmentation
10 papers with code • 6 benchmarks • 0 datasets
Benchmarks
| Trend | Dataset | Best Model | Paper Title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
Datasets
Greatest papers with code
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
To develop a deep learning-based segmentation model for a new image dataset (e. g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches.
Enforcing temporal consistency in Deep Learning segmentation of brain MR images
bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets
•
•
Proposed CNN based segmentation approaches demonstrate how 2D segmentation using prior slices can provide similar results to 3D segmentation while maintaining good continuity in the 3D dimension and improved speed.
3D Medical Imaging Segmentation
4D Spatio Temporal Semantic Segmentation
+2
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning
arnab39/FewShot_GAN-Unet3D
•
•
In addition, our work presents a comprehensive analysis of different GAN architectures for semi-supervised segmentation, showing recent techniques like feature matching to yield a higher performance than conventional adversarial training approaches.
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.
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.
Non-local U-Net for Biomedical Image Segmentation
zhengyang-wang/3D-Unet--Tensorflow
•
•
In this work, we propose the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation.
Using deep convolutional neural networks for neonatal brain image segmentation
Introduction: Deep learning neural networks are especially potent at dealing with structured data, such as images and volumes.
NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
DLTK/models
•
•
NeuroNet is a deep convolutional neural network mimicking multiple popular and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM.
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
GunhoChoi/FusionNet-Pytorch
•
•
Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nano-scale microscopy.
ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging
tbdair/ROAM
•
•
Medical image segmentation is one of the major challenges addressed by machine learning methods.