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Cascade Residual Multiscale Convolution and Mamba-Structured UNet for Advanced Brain Tumor Image Segmentation

Entropy (Basel, Switzerland), 2024-05, Vol.26 (5), p.385

2024

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Constrained Gaussian mixture model framework for automatic segmentation of MR brain images

IEEE transactions on medical imaging, 2006-09, Vol.25 (9), p.1233-1245

2006

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IEEE_Xplore

Investigation of the Role of Feature Selection and Weighted Voting in Random Forests for 3-D Volumetric Segmentation

IEEE transactions on medical imaging, 2014-02, Vol.33 (2), p.258-271

2014

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IEEE_Xplore

White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain Magnetic Resonance Imaging Using Adaptive U-Net and Local Convolutional Neural Network

Computer journal, 2022-12, Vol.65 (12), p.3081-3090

2022

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Non-local-based spatially constrained hierarchical fuzzy C-means method for brain magnetic resonance imaging segmentation

IET image processing, 2016-11, Vol.10 (11), p.865-876

2016

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An atlas-free newborn brain image segmentation and classification scheme based on SOM-DCNN with sparse auto encoder

Computer methods in biomechanics and biomedical engineering., 2020-01, Vol.8 (1), p.49-64

2020

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MR imaging enhancement and segmentation of tumor using fuzzy curvelet

African journal of business management, 2011-11, Vol.6 (31)

M. Arfan Jaffar

2011

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UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

IEEE transactions on medical imaging, 2020-06, Vol.39 (6), p.1856-1867

2020

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IEEE_Xplore

Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning

IEEE transactions on medical imaging, 2018-07, Vol.37 (7), p.1562-1573

2018

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Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

Medical image analysis, 2017-02, Vol.36, p.61-78

2017

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DRINet for Medical Image Segmentation

IEEE transactions on medical imaging, 2018-11, Vol.37 (11), p.2453-2462

2018

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IEEE_Xplore

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

IEEE transactions on medical imaging, 2016-05, Vol.35 (5), p.1240-1251

2016

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IEEE_Xplore

VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

NeuroImage (Orlando, Fla.), 2018-04, Vol.170, p.446-455

2018

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Concatenated and Connected Random Forests With Multiscale Patch Driven Active Contour Model for Automated Brain Tumor Segmentation of MR Images

IEEE transactions on medical imaging, 2018-08, Vol.37 (8), p.1943-1954

2018

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IEEE_Xplore

Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI

Journal of magnetic resonance imaging, 2020-01, Vol.51 (1), p.175-182

2020

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Brain tumor segmentation with Deep Neural Networks

Medical image analysis, 2017-01, Vol.35, p.18-31

2017

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Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

IEEE transactions on medical imaging, 2016-05, Vol.35 (5), p.1252-1261

2016

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IEEE_Xplore

Optimization and comparative evaluation of nonlinear deformation algorithms for atlas-based segmentation of DBS target nuclei

NeuroImage (Orlando, Fla.), 2019-01, Vol.184, p.586-598

2019

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Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Journal of digital imaging, 2017-08, Vol.30 (4), p.449-459

2017

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Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging

IEEE transactions on medical imaging, 2017-11, Vol.36 (11), p.2319-2330

2017

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IEEE_Xplore

Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images

NeuroImage (Orlando, Fla.), 2018-12, Vol.183, p.650-665

2018

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DeepNAT: Deep convolutional neural network for segmenting neuroanatomy

NeuroImage (Orlando, Fla.), 2018-04, Vol.170, p.434-445

2018

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HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation

IEEE transactions on medical imaging, 2019-05, Vol.38 (5), p.1116-1126

2019

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IEEE_Xplore

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study

NeuroImage (Orlando, Fla.), 2018-04, Vol.170, p.456-470

2018

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Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach

NeuroImage (Orlando, Fla.), 2017-07, Vol.155, p.159-168

2017

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Cascade Residual Multiscale Convolution and Mamba-Structured UNet for Advanced Brain Tumor Image Segmentation

Constrained Gaussian mixture model framework for automatic segmentation of MR brain images

Investigation of the Role of Feature Selection and Weighted Voting in Random Forests for 3-D Volumetric Segmentation

White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain Magnetic Resonance Imaging Using Adaptive U-Net and Local Convolutional Neural Network

Non-local-based spatially constrained hierarchical fuzzy C-means method for brain magnetic resonance imaging segmentation

An atlas-free newborn brain image segmentation and classification scheme based on SOM-DCNN with sparse auto encoder

MR imaging enhancement and segmentation of tumor using fuzzy curvelet

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

DRINet for Medical Image Segmentation

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

Concatenated and Connected Random Forests With Multiscale Patch Driven Active Contour Model for Automated Brain Tumor Segmentation of MR Images

Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI

Brain tumor segmentation with Deep Neural Networks

Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

Optimization and comparative evaluation of nonlinear deformation algorithms for atlas-based segmentation of DBS target nuclei

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging

Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy

HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study

Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach

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