Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging
A Danelakis, T Theoharis, DA Verganelakis - … Medical Imaging and …, 2018 - Elsevier
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 …
Global image registration using a symmetric block-matching approach
Most medical image registration algorithms suffer from a directionality bias that has been
shown to largely impact subsequent analyses. Several approaches have been proposed in …
shown to largely impact subsequent analyses. Several approaches have been proposed in …
In silico mathematical modelling for glioblastoma: a critical review and a patient-specific case
Glioblastoma extensively infiltrates the brain; despite surgery and aggressive therapies, the
prognosis is poor. A multidisciplinary approach combining mathematical, clinical and …
prognosis is poor. A multidisciplinary approach combining mathematical, clinical and …
Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure
O Commowick, A Istace, M Kain, B Laurent, F Leray… - Scientific reports, 2018 - nature.com
We present a study of multiple sclerosis segmentation algorithms conducted at the
international MICCAI 2016 challenge. This challenge was operated using a new open …
international MICCAI 2016 challenge. This challenge was operated using a new open …
Deep-learning-based multi-modal fusion for fast MR reconstruction
T1-weighted image (T1WI) and T2-weighted image (T2WI) are the two routinely acquired
magnetic resonance (MR) modalities that can provide complementary information for clinical …
magnetic resonance (MR) modalities that can provide complementary information for clinical …
[HTML][HTML] One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
In recent years, several convolutional neural network (CNN) methods have been proposed
for the automated white matter lesion segmentation of multiple sclerosis (MS) patient …
for the automated white matter lesion segmentation of multiple sclerosis (MS) patient …
SoftSeg: Advantages of soft versus binary training for image segmentation
Most image segmentation algorithms are trained on binary masks formulated as a
classification task per pixel. However, in applications such as medical imaging, this “black …
classification task per pixel. However, in applications such as medical imaging, this “black …
[HTML][HTML] Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset
O Commowick, M Kain, R Casey, R Ameli, JC Ferré… - Neuroimage, 2021 - Elsevier
MRI plays a crucial role in multiple sclerosis diagnostic and patient follow-up. In particular,
the delineation of T2-FLAIR hyperintense lesions is crucial although mostly performed …
the delineation of T2-FLAIR hyperintense lesions is crucial although mostly performed …
DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation
RA Kamraoui, VT Ta, T Tourdias, B Mansencal… - Medical Image …, 2022 - Elsevier
Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed
promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These …
promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These …
White matter abnormalities in depression: A categorical and phenotypic diffusion MRI study
Mood depressive disorder is one of the most disabling chronic diseases with a high rate of
everyday life disability that affects 350 million people around the world. Recent advances in …
everyday life disability that affects 350 million people around the world. Recent advances in …