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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
TLDR
This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Expand
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A review of atlas-based segmentation for magnetic resonance brain images
TLDR
A review of the automated approaches for atlas-based segmentation of magnetic resonance brain images. Expand
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Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach
TLDR
We propose an automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Expand
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Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches
TLDR
Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years. Expand
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Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
TLDR
We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. Expand
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Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations
TLDR
We compare the accuracy of 10 brain tissue segmentation methods analyzing the effects of SCSF ground‐truth voxels on accuracy estimations. Expand
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A toolbox for multiple sclerosis lesion segmentation
IntroductionLesion segmentation plays an important role in the diagnosis and follow-up of multiple sclerosis (MS). This task is very time-consuming and subject to intra- and inter-rater variability.Expand
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Automated sub‐cortical brain structure segmentation combining spatial and deep convolutional features
TLDR
We propose a CNN approach for sub‐cortical brain structure segmentation that combines convolutional and spatial features for improving the segmentation accuracy. Expand
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Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding
TLDR
We present a modified expectation-maximisation algorithm to segment brain tissues (white matter, grey matter, and cerebro-spinal fluid) as well as a partial volume class containing fluid and grey matter. Expand
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One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
TLDR
We investigated the transferability of the CNN model when applied to other MRI scanners and protocols, evaluating the minimum number of annotated images needed to re-train to obtain comparable accuracy. Expand
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