Abstract:
White matter hyperintensities (WMHs) are high-intensity pixels in MRI, especially in
FLAIR images. The presence of white matter hyperintensitiesn brain MRI is associated
with cerebral small vessel diseases (CSVD), aging-related brain diseases, strokes, and
dementia. The extraction of hyperrintensities from FLAIR MRI images of the brain is
called WMH segmentation, which is important because it provides details about the
volume, location, and shape of WMH lesions. These measurements and the quantity of
WMHs are important in the research and diagnosis of a patient. In usual practice, the field
expert (radiologist) performs manual segmentation of medical imaging, which is time-
consuming, costly, and subjective. A few years ago, several different semi-automated and
fully automated methods were proposed for different tasks such as WHM detection, stroke
lesion segmentation, and WMH segmentation. Some of these methods use supervised
learning algorithms with handcrafted features or more recently learned features
(representations). On the other hand, some methods also use unsupervised learning for
segmentation. These days, deep neural networks are quite popular and robust for learning
problems and claim more than human performance in several problems. A convolutional
neural network is a deep network structure and is very attractive and effective in computer
vision domain problems, especially as a robust method for image classification and
segmentation. Recently, many algorithms have been proposed for WMH segmentation,
including CNN, but the automatic WMH segmentation task is still challenging due to
coexisting with other abnormalities and spatial variability of white matter lesions. This
study used an ensemble model of three variants of the U-NET based models. A U-NET
network is a network-advanced version of CNN for bio-medical image segmentation tasks.
