Brain Segmentation
34 papers with code • 1 benchmarks • 2 datasets
Benchmarks
| Trend | Dataset | Best Model | Paper Title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
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Tc1 Mouse cerebellum atlas
Multi-template MRI mouse brain atlas
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.
HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation
black0017/MedicalZooPytorch
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Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation.
Ranked #1 on
Medical Image Segmentation
on iSEG 2017 Challenge
3D Densely Convolutional Networks for VolumetricSegmentation
black0017/MedicalZooPytorch
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arXiv preprint 2017
The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
mateuszbuda/brain-segmentation-pytorch
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Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.
Ranked #2 on
Brain Segmentation
on Brain MRI segmentation
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning
arnab39/FewShot_GAN-Unet3D
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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.
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.
`Project & Excite' Modules for Segmentation of Volumetric Medical Scans
abhi4ssj/squeeze_and_excitation
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Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging.
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
ai-med/squeeze_and_excitation
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Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications.