Deep learning for brain MRI segmentation: state of the art and future directions
Quantitative analysis of brain MRI is routine for many neurological diseases and conditions
and relies on accurate segmentation of structures of interest. Deep learning-based …
and relies on accurate segmentation of structures of interest. Deep learning-based …
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 …
Convolutional neural network in medical image analysis: a review
Medical image analysis helps in resolving clinical issues by examining clinically generated
images. In today's world of deep learning (DL) along with advances in computer vision, the …
images. In today's world of deep learning (DL) along with advances in computer vision, the …
Optimal Integration of Machine Learning for Distinct Classification and Activity State Determination in Multiple Sclerosis and Neuromyelitis Optica
The intricate neuroinflammatory diseases multiple sclerosis (MS) and neuromyelitis optica
(NMO) often present similar clinical symptoms, creating challenges in their precise detection …
(NMO) often present similar clinical symptoms, creating challenges in their precise detection …
Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging
Multiple sclerosis (MS) is a chronic disease. It affects the central nervous system and its
clinical manifestation can variate. Magnetic Resonance Imaging (MRI) is often used to …
clinical manifestation can variate. Magnetic Resonance Imaging (MRI) is often used to …
[HTML][HTML] Automated segmentation of changes in FLAIR-hyperintense white matter lesions in multiple sclerosis on serial magnetic resonance imaging
P Schmidt, V Pongratz, P Küster, D Meier, J Wuerfel… - NeuroImage: Clinical, 2019 - Elsevier
Longitudinal analysis of white matter lesion changes on serial MRI has become an important
parameter to study diseases with white-matter lesions. Here, we build on earlier work on …
parameter to study diseases with white-matter lesions. Here, we build on earlier work on …
[HTML][HTML] A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis
Introduction: Longitudinal magnetic resonance imaging (MRI) has an important role in
multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new T2-w …
multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new T2-w …
[HTML][HTML] Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data–a systematic review
R Balakrishnan, MCV Hernández, AJ Farrall - … Medical Imaging and …, 2021 - Elsevier
Background White matter hyperintensities (WMH), of presumed vascular origin, are visible
and quantifiable neuroradiological markers of brain parenchymal change. These changes …
and quantifiable neuroradiological markers of brain parenchymal change. These changes …
A dense residual U-net for multiple sclerosis lesions segmentation from multi-sequence 3D MR images
Multiple Sclerosis (MS) is an autoimmune disease that causes brain and spinal cord lesions,
which magnetic resonance imaging (MRI) can detect and characterize. Recently, deep …
which magnetic resonance imaging (MRI) can detect and characterize. Recently, deep …
Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review
M Diaz-Hurtado, E Martínez-Heras, E Solana… - Neuroradiology, 2022 - Springer
Multiple sclerosis (MS) is a chronic autoimmune disease characterized by demyelinating
lesions that are often visible on magnetic resonance imaging (MRI). Segmentation of these …
lesions that are often visible on magnetic resonance imaging (MRI). Segmentation of these …