Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin
is of key importance in many neurological research studies. Currently, measurements are …

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML),
a branch of the wider field of artificial intelligence, it is possible to extract patterns within …

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 …

Recent years have seen increasing use of supervised learning methods for segmentation
tasks. However, the predictive performance of these algorithms depends on the quality of …

In recent years, there have been multiple works of literature reviewing methods for
automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature …

In recent years, several convolutional neural network (CNN) methods have been proposed
for the automated white matter lesion segmentation of multiple sclerosis (MS) patient …

Automated computed tomography (CT) scan segmentation (labelling of pixels according to
tissue type) is now possible. This technique is being adapted to achieve three‐dimensional …

Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the
training, validation, and testing of advanced deep learning (DL)-based automated tools …