Brain Segmentation
( Image credit: 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study )
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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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.
BRAIN IMAGE SEGMENTATION BRAIN SEGMENTATION FEW-SHOT SEMANTIC SEGMENTATION IMAGE REGISTRATION ZERO SHOT 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.
3D MEDICAL IMAGING SEGMENTATION BRAIN IMAGE SEGMENTATION BRAIN SEGMENTATION FEW-SHOT SEMANTIC SEGMENTATION SEMI-SUPERVISED SEMANTIC SEGMENTATION
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.
3D MEDICAL IMAGING SEGMENTATION BRAIN SEGMENTATION BRAIN TUMOR SEGMENTATION
3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data.
`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.
BRAIN SEGMENTATION IMAGE CLASSIFICATION SEMANTIC SEGMENTATION
HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation
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.
BRAIN SEGMENTATION IMAGE CLASSIFICATION MULTI-MODAL IMAGE SEGMENTATION REPRESENTATION LEARNING SEMANTIC SEGMENTATION
Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control
Next to voxel-wise uncertainty, we introduce four metrics to quantify structure-wise uncertainty in segmentation for quality control.
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a \revision{MRI brain scan} in 20 seconds.
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location.