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 …
[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 …
[HTML][HTML] Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation
Accurate lung nodule segmentation from computed tomography (CT) images is of great
importance for image-driven lung cancer analysis. However, the heterogeneity of lung …
importance for image-driven lung cancer analysis. However, the heterogeneity of lung …
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 …
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 …
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 …
Dual-branch residual network for lung nodule segmentation
H Cao, H Liu, E Song, CC Hung, G Ma, X Xu, R Jin… - Applied Soft …, 2020 - Elsevier
An accurate segmentation of lung nodules in computed tomography (CT) images is critical to
lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the …
lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the …
Neural stem cell transplantation in patients with progressive multiple sclerosis: an open-label, phase 1 study
A Genchi, E Brambilla, F Sangalli, M Radaelli… - Nature medicine, 2023 - nature.com
Innovative pro-regenerative treatment strategies for progressive multiple sclerosis (PMS),
combining neuroprotection and immunomodulation, represent an unmet need. Neural …
combining neuroprotection and immunomodulation, represent an unmet need. Neural …
[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 …
A deep learning approach to predicting disease progression in multiple sclerosis using magnetic resonance imaging
L Storelli, M Azzimonti, M Gueye, C Vizzino… - Investigative …, 2022 - journals.lww.com
Objectives Magnetic resonance imaging (MRI) is an important tool for diagnosis and
monitoring of disease course in multiple sclerosis (MS). However, its prognostic value for …
monitoring of disease course in multiple sclerosis (MS). However, its prognostic value for …