# MR Images, Brain Lesions, and Deep Learning
###### Abstract  
Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment. deep learning; machine learning; ischemic stroke; demyelinating disease; image processing; computer-aided diagnostics; brain MRI; CNN; white matter hyperintensities; VOSviewer +
Footnote †: journal: Applied  
anomalies [12] and also to better interpret brain images [13] and analyze complex brain imaging data [14]. In terms of neuroimaging of both normal tissues and pathologies, there are different modalities, namely (1) computed tomography (CT) and magnetic resonance imaging (MRI), which are commonly used for the structural visualization of the brain; (2) positron emission tomography (PET), used principally for physiological analysis; and (3) single-photon emission tomography (SPECT) and functional MRI, which are used for functional analysis of the brain [15]. MRI and CT are preferred by radiologists to understand brain pathologies [12]. Due to continual advancements in MRI technology, it considered to be a promising tool that can elucidate the brain structure and function [3], e.g., brain MR image resolution has grown by leaps and bounds since the first MR image acquisition [16]. Because of this reason, this modality is frequently used (more than CT) to examine anatomical brain structures, perform visual inspection of cranial nerves, and examine abnormalities of the posterior fossa and spinal cord [17]. Another advantage of MRI compared to CT is that MRI is less susceptible to artifacts in the image [18]. In addition, MR image analysis is useful different tasks, e.g., lesion detection, lesion segmentation, tissue segmentation, and brain parcellation on neonatal, infant, and adult subjects [4,5]. In this work, we discuss MR image processing to detect, segment, and classify white matter hyperintensities (WMHs) using techniques of artificial intelligence. [PERSON] et al. [19] state that WMHs are seen in MRI studies of neurological disorders like multiple sclerosis, dementia, stroke, cerebral small-vessel disease (SVD), and Parkinson's disease [19,20]. According to [PERSON] et al. [20], due to a lack of pathological studies, the etiology of WMHs is frequently proposed to be of an ischemic or a demyelinating nature. A WMH is termed ischemia if caused by an obstruction of a blood vessel, and a WMH is considered to be demyelinating when there is an inflammation that causes destruction and loss of the myelin layer and compromises neural transmission [20,21, 22], and this type of WMH is often related to multiple sclerosis (MS) [8,22] (Figure 1). A stroke occurs when the blood flow to an area of the brain is interrupted [21,23].