Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
In recent years, deep convolutional neural networks (CNNs) have shown record-shattering
performance in a variety of computer vision problems, such as visual object recognition …
performance in a variety of computer vision problems, such as visual object recognition …
3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
This study investigates a 3D and fully convolutional neural network (CNN) for subcortical
brain structure segmentation in MRI. 3D CNN architectures have been generally avoided …
brain structure segmentation in MRI. 3D CNN architectures have been generally avoided …
HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation
Recently, dense connections have attracted substantial attention in computer vision
because they facilitate gradient flow and implicit deep supervision during training …
because they facilitate gradient flow and implicit deep supervision during training …
Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach
In this paper, we present a novel automated method for White Matter (WM) lesion
segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a …
segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a …
[HTML][HTML] Evaluating white matter lesion segmentations with refined Sørensen-Dice analysis
The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image
segmentation algorithms. It offers a standardized measure of segmentation accuracy which …
segmentation algorithms. It offers a standardized measure of segmentation accuracy which …
Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks
The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS)
patients. Segmentation of the spinal cord and lesions from MRI data provides measures of …
patients. Segmentation of the spinal cord and lesions from MRI data provides measures of …
Longitudinal multiple sclerosis lesion segmentation: resource and challenge
In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion
segmentation challenge providing training and test data to registered participants. The …
segmentation challenge providing training and test data to registered participants. The …
Random forest regression for magnetic resonance image synthesis
By choosing different pulse sequences and their parameters, magnetic resonance imaging
(MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield …
(MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield …
[HTML][HTML] Multiple sclerosis diagnosis using machine learning and deep learning: challenges and opportunities
Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which
can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated …
can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated …
[HTML][HTML] Structural neuroimaging as clinical predictor: A review of machine learning applications
In this paper, we provide an extensive overview of machine learning techniques applied to
structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We …
structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We …