key: cord-0116324-d86o9w93 authors: Castillo, Darwin; Lakshminarayanan, Vasudevan; Rodriguez-Alvarez, Maria J. title: MRI Images, Brain Lesions and Deep Learning date: 2021-01-13 journal: nan DOI: nan sha: 9570723d6e4da511a206f4ea85182dd22abb2616 doc_id: 116324 cord_uid: d86o9w93 Medical brain image analysis is a necessary step in Computer Assisted /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 MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out 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 proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment. There are estimated to be as many as a billion people worldwide [1] affected by peripheral and central neurological disorders [1, 2] . Some of these disorders include: brain tumors, Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis (MS), epilepsy, dementia, neuroinfectious, stroke, and traumatic brain injuries [1] . According to the World Health Organization (WHO): ischemic stroke and "Alzheimer disease with other dementias" are the second and fifth major causes of death, respectively [2] . Biomedical images give fundamental information necessary for the diagnosis, prognosis, and treatment of different pathologies. Of all the various imaging modalities neuroimaging, is a crucial tool for studying the brain [3] [4] [5] [6] [7] [8] [9] . In terms of neuroimaging of functional MRI which are used for functional analysis of the brain [10] . Brain MRI image analysis is useful for different tasks, e.g.: lesion detection, lesion segmentation, tissue segmentation, as well as brain parcellation on neonatal, infant, and adult subjects [3, 11] . MRI is frequently used in the visual inspection of cranial nerves, abnormalities of the posterior fossa and spinal cord [12] , since it is less susceptible to artifacts in the image when compared to CT. This paper is organized as follows. Section 2 gives an outline of the selection criteria adopted for the literature review. Section 3 describes the principal machine learning and deep learning methods used in this application, and Section 4, summarizes the principal constraints, and common problems encountered in these CAD systems and we conclude this section 5 with a brief discussion. The literature review was conducted using the recommendations given by Khan et al., [46] as well as the methodology proposed by Torres-Carrión [47, 48] . We generated and analyzed bibliometric maps and identified clusters and their reference networks [49, 50] . We also used the methods given in [51, 52] to identify the strength of the research, as well as authors and principal research centers that work in the MRI images which use machine and deep learning for the identification of brain diseases. The bibliometric analysis was performed by searching for the relevant literature using the following bibliographic databases [24] : Scopus [53] , PubMed [54], Web of Science (WOS) [55] , Science Direct [56] , IEEE Xplore [57] , and Google Scholar [58] . In order to perform an appropriate search it is important to focus our attention on the real theoretical context of the research, for which reason the method proposed by Torres-Carrión [48] a so-called "conceptual mindfact" (mentefacto conceptual), can help to organize the scientific thesaurus of the research theme [47] . Figure 2 describes the conceptual mindfact used in this work to focus and constraint the topic to MRI Brain Algorithm difference Ischemic and Demyelinating diseases and obtain an adequate semantic search structure of the literature in the relevant scientific databases. Table 1 presents the semantic search structure [48] such as the input of the search specific literature (documents) in the scientific databases. The first layer is an abstraction of the conceptual mindfact; the second corresponds to the specific technicality namely, Brain Processing; the third level is relevant to the application, namely, the ischemic and demyelinating diseases. The fourth level is the global semantic structure search. The result of the global semantic structure search (Fig 2) In order to analyze and answer the three central research questions of this work, the global search of the 140 documents was further refined. This filter of research complied with the categories given by Fourcade and Khonsari [61] , and were applied only to the "article" documents. These criteria were; • aim of the study: ischemia and demyelinating processing MRI brain images, identification, detection, classification or differentiate between them. • methods: algorithms of machine learning, deep learning, neural network architectures, dataset, training, validations, testing. • results: performance metrics, accuracy, sensibility, specificity, dice coefficient. • conclusions: challenges, open problems, recommendations, future. According to the second selection criteria, we found 38 documents to include in the analysis of this work and also were related to and in agreement with the items described above. For analysis we used VOSviewer version 1.6.15 software [50] order to construct and display bibliometric maps. The data used for this objective was searched in Scopus due to its coverage of a wider range of journals [49, 62] . In terms of citations and the countries of origin of these publications (Fig 5) , we observe that the United States has a large number of citations, followed by Germany, India and the United Kingdom. This relationship was determined by the analysis of the number of citations in the documents generated by country, in agreement with the affiliation of the (primary authors? Corresponding authors?)authors and for each country the total strength of the citation link [51] . The minimum number of documents of any individual country was five and the minimum number of citations a country received was one. The with the year of publication and, the diameter of the points shows the normalization of the citations according to Van Eck and Waltman [52, 63] . The purple points are the documents that have fewer than ten citations and yellow represents documents with more than 60 citations. In Table 2 , we list the ten most cited articles according to the normalization of the citations [63] . Waltman et al [51] manifest that "the normalization corrects for the fact that older documents have had more time to receive citations than more recent documents" [51, 64] . Also, table two show the dataset, methodology, techniques and metrics used to develop and validate the algorithm or CAD systems proposed by theses authors. In the bibliometric networks or science mapping there are large differences between nodes in the number of edges they have to other nodes [50] . In order to reduce these differences, the VOSviewer uses the association strength normalization [63] , that is a probabilistic measure of the co-occurrence data. The association strength normalization is discussed by Van Eck and Waltman [63] , and here we construct a normalized network [50] in which the weight of the edge between nodes i and j is given by: is also known as the similarity of nodes i and j, ( ) denotes the total weight of all edges of node i (node j) and m denotes the total weight of all edges in the network [50] . and For more information related with normalization, mapping and clustering techniques used by VOSviewer, the reader is referred to the relevant literature [50, 63, 64] . From Table 2 it can be seen that articles that are cited often deal with ischemic stroke rather than demyelinating disease. According with the methods and techniques used were support vector machine (SVM) [65] , random forest (RF) [32] ; classical algorithms of segmentation like Watershed algorithm (WS) [66] ; and techniques of deep learning such as convolutional neural networks (CNN) [36, 67] ; as well as a combination between them: SVM-RF [22] , CNN-RF [20, 68] . In the following subsections, we discuss how artificial intelligence (AI) through ML and DL methods are used in the development of algorithms for brain disease diagnosis and their relation to the central theme of this review. The definitions of machine learning and deep learning are part of the global field of the Artificial Intelligence (AI) which is defined as the ability for a computer to imitate the cognitive abilities of a human being [61] . There are two different general concepts of AI: (1) Cognitivism related with development of rule-based programs referred to as expert systems, and (2) Connectionism associated to the development of simple programs educated or trained by data [61, 69] . applications of AI to medicine and health are not covered, e.g., ophthalmology where AI has had tremendous success, see [70] [71] [72] [73] [74] [75] . Machine Learning (ML) can be considered as a subfield of artificial intelligence (AI). Lundervold and Lundervold [76] and Noguerol et al., [77] state that the main aim of ML is to develop mathematical models and computational algorithms with the ability to solve problems by learning from experiences without or with the minimum possible human intervention, in other words the model created will be able to be trained to produce useful outputs when fed input data [77] . Lakhani et al., [78] state that recent studies demonstrate that machine learning algorithms give accurate results for the determination of study protocols for both brain and body MRIs. Machine learning can be classified into (1) supervised learning methods (e.g. support vector machine, decision tree, logistic regression, linear regression, naive Bayes and random forest), and (2) unsupervised learning methods (K-means, mean shift, affinity propagation, hierarchical clustering, and Gaussian mixture modeling) [79] (Fig. 8) . Algorithm used for tasks of classify, regression and clustering. SVM is driven by a linear function similar to logistic regression [80] , but with the difference that SVM only outputs class identities and does not provide probabilities. SVM classifies between two classes by constructing a hyperplane in highdimensional feature space [81] . The class identities are positive or negative when is positive or negative, respectively. For the optimal separating hyperplane between classes, the SVM uses different kernels (dot products) [82, 83] . More information and detail about the SVM is given in the literature [80] [81] [82] [83] . The k-NN is a non-parametric algorithm (it means no assumption for underlying data distribution) and can be used for classification or regression [80, 84] . Like a classifier k-NN is based on the measure of the Euclidean distance (distance function) and a voting function in k nearest neighbors [85] given N training vectors. The value of the k (the number of nearest neighbors) decides the classification of the points between classes. KNN has the following basic steps: (1) Calculate distance, (2) Find closest neighbors and (3) Vote for labels [84] . More details of the k-NN algorithm can be found in references [80, 85, 86] . Programming libraries such a Scikit-Learn have algorithms for k_NN [84] . The k-NN has higher accuracy and stability for MRI data, but is relatively slow in terms of computational time [86] . As an aside it is interesting to note that the nearest neighbor formulation might have been first described by the Islamic polymath Optics ", see: [87] )! Trees [88] . Here a forest of classification trees is generated where each tree is grown on a bootstrap sample of the data [89] . In that way, the RF classifier consists of a collection of binary classifiers where each decision tree casts a unit vote for the most popular class label (see figure 8 (d) ) [90] . More information are given elsewhere [91] . The k-means clustering algorithm is used for segmentation in medical imaging due to its relatively low computational complexity [92, 93] and minimum computation time [94] . It is an unsupervised algorithm based on the concept of clustering. Clustering is a technique of grouping pixels of an image according to their intensity values [95, 96] , It divides the training set into k different clusters of examples that are near each other [80] . The properties of the clustering are measure such as the average Euclidean distance from a cluster centroid to the members of the cluster [80] . The input data for use with this algorithm should be numeric values, with continuous being better than discrete values, and the algorithm performs well when used with unlabeled datasets. Deep Learning (DL) is a subfield of ML [97] that uses artificial neural networks (ANN) to develop decision making algorithms [77] . Artificial Neural Networks are neural networks which employ learning algorithm [98] and infer rules for learning, In order to do so a set of training data examples are needed, the concept is derived from the concept of the biological neuron concept ( figure 8 (e) ). An artificial neuron receives inputs from other neurons, integrates the inputs with weights, and activates (or "fires" in the language of biology) when a pre-defined condition is satisfied [79] . There are many books describing AANs -see for example [80] . The fundamental unit of a neural network is the neuron, which has a bias w0 and a weight vector w = (w1, . . ., wn) as parameters θ = (w0,...,wn) to model a decision: using a non-linear activation function h(x) [99] . The activation functions commonly used are: function, the sigmoid function and An interconnected group of nodes comprise the ANN, where each node representing a neuron arranged in layers [76] , the arrow representing a connection from the output of one neuron to the input of another [90] . ANNs have input layer which receives observed values, while the output layer represents the target (a value or class) and the layers between input and output layers are called hidden layers [79] . There are different types of ANNs [100] and the most common types are: convolutional neural nets (CNNs) [101] , recurrent neural nets (RNN) [102] , long short-term memory(LSTM) [103] , and generative adversarial networks (GANs) [104] . In practice, these types of networks can be combined [100] between them and with classical machine learning algorithms.. The CNNs are most commonly used for the processing of medical images because of their success in processing and recognition of patterns in vision systems [43] . CNNs are inspired by the biological visual cortex and also called multi-layer perceptrons (MLPs) [43, 105, 106] . It consists of a stack of layers: convolutional, max pooling and fully connected layers. The intermediate layer is fed by the output of the previous layer e.g. the convolutional layer creates a feature map of different size and the pooling layers reduce the size of feature maps to be feed to the following layers. The final fully connected layers produce the specified class prediction at the output [43] . The general CNN architecture is presented in Fig 9. There is a compromise between the numbers of neurons in each layer, the connection between them and the number of layers with the number of parameters that defines the network [43] . [115] , Le at al., [116] , Shen et al., [105] . Computer-aided diagnosis has its origins in 1980s at the Kurt Rossmann Laboratories for Radiologic Image Research in the Department of Radiology at the University of Chicago, [117] . The initial work was on detection of breast cancer [29, 117, 118] and the reader is referred to a recent review [119] . There has been much research and development of CADx systems using different modalities of medical images. The CAD not is a substitute for the specialist but can assist or be an adjunct to the specialist in the interpretation of the images [34] . In other words, CADx systems can provide a "second objective opinion" [89, 99] and make the final disease decision from image-based information and the discrimination of lesions, complementing a radiologist's assessment [120] . CAD development takes into consideration the principles of radiomics [40, [121] [122] [123] [124] [125] . The term radiomics is defined as the extraction and analysis of quantitative features of medical images, in other words the conversion of medical images into mineable data with high fidelity and high throughput for decision support [40, 121, 122] . The medical images used in radiomics are obtained principally with CT, PET or MRI [40] . The steps that are utilized by a CAD system consists of [40] : (a) image data and preprocessing, (b) image segmentation, (c) feature extraction and qualification, (d) classification (Fig 10) . The dataset is the principal component to develop an algorithm because it is the nucleus of the processing. Razzak et al., [109] state that the accuracy of diagnoses of the disease depends upon image acquisition and image interpretation. However Shen et al., [105] add a caveat that the image features obtained from one method need not be guaranteed for other images acquired using different equipment [105, 126, 127] . For example, it has been shown that the methods of image segmentation and registration designed for 1.5-Tesla T1-weighted brain MR images are not applicable to 7.0-Tesla T1weighted MR images [43, 57, 58 ]. There are different datasets of images for brain medical image processing, in the case of stroke, the most famous datasets used is the ISLES (Ischemic Stroke Lesion Segmentation) dataset [20, 68] and ATLAS (Anatomical Tracings of Lesions After Stroke) [128] ; for the case of demyelinating disease there isn't a specific dataset, but datasets for Multiple Sclerosis is used, e.g., MSSEG (MS segmentation) [129] . Table 3 describes the datasets that have been used in the publications under consideration in this review is possible find datasets for brain medical image processing. There are several preprocessing steps necessary to reduce the noise and artifacts in the medical images, before the segmentation [34, 130, 131] . The preprocessing steps commonly used are (1) the grayscale conversion, and the image resizing [131] to get better contrast and enhancement, (2) bias field correction to correct the intensity inhomogeneity [24, 130] , (3) image registration, a process for spatial alignment [130] , and (4) removal of nonbrain tissue such as fat, skull, or neck which have intensities overlapping with intensities of brain tissues [21, 130, 132] . In simple terms image segmentation is the procedure of separating a digital image into a different set of pixels [31] and is considered the most fundamental process as it extracts the region of interest (ROI) through a semiautomatic or automatic process [133] . It divides the image into areas according a specific description to obtain the anatomical structures and patterns of diseases. Despotovíc et al., [130] , Merjulah and Chandra [31] indicate that the principal goal of the medical image segmentation is to make things simpler and transform it into a set of semantically meaningful, homogeneous, and nonoverlapping regions of similar attributes such as intensity, depth, color, or texture [130] because the segmentation assists doctors to diagnose and make decisions [31] . According to Despotovíc et al., [130] the segmentation methods for brain MRI are classified into: (i) manual segmentation, (ii) intensity-based methods (including thresholding, region growing, classification, and clustering), (iii) atlas-based methods, (iv) surface-based methods (including active contours and surfaces, and multiphase active contours), and (v) hybrid segmentation methods [130] . To evaluate, validate and measure the performance of every automated lesion segmentation methodology compared to the expert segmentation [134] one needs to consider the accuracy (evaluation measurements) and reproducibility of the model [135] . The evaluation measurements compare the output of segmentation algorithms with ground truth in either a pixel-wise or a volume-wise basis [3] . The accuracy is related with the grade of closeness of the estimated measure to the true measure [135] , • Precision: is the measure of over-segmentation between 0 and 1, and it means the proportion of the computed segmentation which overlaps with the reference segmentation [136, 137] , This is also is called the positive predictive value (PPV), with a high PPV indicating that a patient identified with a lesion does actually have the lesion [139] . • Recall also known as Sensitivity: Gives a metric between 0 and 1, this a sign of over-segmentation, and it is a measure of the amount of the reference segmentation which overlaps with the computed segmentation [136, 137] . The metrics of overlap measures which are less often used are the sensitivity, specificity (measures the portion of negative voxels in the ground truth segmentation [140] ) and accuracy, which according with García-Lorenzo et al., [135] and Taha and Hanbury [140] , should be considered carefully because they penalize errors in small segments more than in large segments. These are defined as: • Average Symmetric Surface Distance (ASSD, mm): represents the average surface distance between two segmentations (computed and reference and vice-versa), and is an indicator of how well the boundaries of the two segmentations align. ASSD is measured in millimeters, and a smaller value indicates higher accuracy [68, 134, 137] . The average surface distance (ASD) is given as: Where is a 3D matrix consisting of the Euclidean distances between the two image volumes and , and is defined [134] : • Hausdorff's distance (HD,mm): It is more sensitive to segmentation errors appearing away from segmentation frontiers than ASSD [137] . The Hausdorff measure is an indicator of the maximal distance between the surfaces of two image volumes (the computed and reference segmentations) [20, 137] . HD is measured in millimeters and like the ASSD a smaller value indicates higher accuracy [134] . where and are points of lesion segmentations and , respectively, and is a 3D matrix consisting of all Euclidean distances between theses points [134] . • Intra Class Correlation (ICC): Is a measure of correlation between volumes segmented and ground truth lesion volume [137] . • Correlation with Fazekas score: A Fazekas score is a clinical measure of WMH, comprising of two integers in the range [0, 3] reflecting the degree of periventricular WMH and deep WMH respectively [137] . • Relative Volume difference (VD, %): It measure the agreement between lesion volume and the ground truth lesion volume, a low VD means more agreement [139, 141] . where, and are the segmented and ground truth lesion volumes respectively. Lastly, we define [135] the "reproducibility" which is a measure of the degree of agreement between several identical experiments. Reproducibility guarantees that differences in segmentations as a function of time result from changes in the pathology and not from the variability of the automatic method [135] . Tables 2 and 4 presents the types of databases, modalities and the evaluation measurements considered and applied to the results reported in the literature to date. An ML or DL algorithm is often a classifier [113] of objects (e.g. lesions in medical images). Feature selection is a fundamental step in the processing of medical image and more specially it allows us to research which features are relevant for the specific classification problem of interest, and also it helps to get higher accuracy rates [42] . The task of feature extraction is complex due to the task of determining to determine an algorithm that can extract a distinctive and complete feature representation, and for that principal reason it is very difficult to generalize and implies that one has to design a featurization method for every new application [99] . In DL, this process is also denoted to as "hand-crafting" features [99] . The classification is related to the extracted features that are entered as input to an ML model [113] , while a DL algorithm model uses pixel values in images directly as input information instead of features calculated from segmented objects [113] . In the case of processing stroke with CNNs the featurization of the images is a key application [68, 142] and depends on the signal-to-noise ratio in the image, which can be improved by target identification via segmentation to select regions of interest [142] . According to Praveen et al., [143] , a CNN learns to discriminative local features and return better performance than handcrafted features. Texture analysis is a common technique in medical pattern recognition tasks to determine the features, and for that one uses second-order statistics or co-occurrence matrix features [40] . Mitra et al., [139] , indicate that they derive local features, spatial features and context-rich features from the input MRI channels. It is clear that currently the DL algorithms especially those that use of a combination of CNNs and machine learning classifiers produce a marked transformation [144] in the featurization and the segmentation in medical image processing [76, 142] . CNNs have a high utility in tasks like identification of compositional hierarchy features and low-level features (e.g. edges), specific pattern forms and intrinsic structures (e.g. shapes, textures) can be developed [3] and spatial features generated from an n-dimensional array of basically any arbitrary size [37, 108] . e.g. the U-Net model proposed by Ronneberger et al., [145] , employed parameter sharing between encoder-decoder paths for incorporating spatial and semantic data that allow better segmentation performance [136] . Based on the U-Net model, currently there are novel variants of U-Net designs, e.g. Bamba et al., [146] , used a U-net architecture with 3D convolutions that allow the use of an attention gate for the decoder to suppress unimported parts of the input while emphasize the relevant features. There is considerable room for improvement and innovation of innovative networks (e.g., [147] ). The process of converting a raw signal into a predictor (automatization of the featurization) constitutes an advantage of the DL methods over others, which is useful when there are large volumes of data of uncertain relationship to an outcome [142] , e.g. the featurization of acute stroke and the demyelinating diseases. In this subsection we discuss the different classifiers that have been utilized in the literature under. Additional details such as dataset and the measure metrics of the algorithms and the tasks are presented in the Tables 2 and 4. In the table 4 is noted that even though there are a large number of publications related to stroke ischemia (27 documents) and most deal with classification of stroke patients versus normal controls, or prediction of post-stroke functional impairment or treatment outcome [15, 19, 20, 22, 27, 32, 36, 59, 60, 65, 67, 68, 131, 132, 136, 143, [148] [149] [150] [151] [152] [153] [154] [155] [156] [157] [158] , there is a paucity of results related to demyelinating disease alone. However there are some publications dealing with Multiple Sclerosis (MS) which is the most common demyelinating disease (2) [159, 160] . In addition, there are articles related to WMHs (5) as well as articles that combine the ischemic stroke with MS and other brain injuries like gliomas (4) [30, 137, 164, 165] . Different studies [15, 65, 79, 158] related to stroke (see table 4 and fig 1) in their different types, use principally classifiers of ML to determine the properties of the lesion. The classifiers most commonly used are SVM and Random Forest (RF) [158] . According to Lee et al., [158] the RF has some advantages over the SVM because RF can be trained quickly and provides insight into the features that can predict the target outcome [158] ; also the RF can automatically perform the task of feature selection and provide a reliable feature importance estimate. Additionally, the SVM is effective only in cases where the number of samples is small compared with the number of features [79, 158] . Along similar lines, Subudhi et al., [22] reported that the RF algorithm works better when one has a large dataset and it is more robust when there are a higher number of trees in the decision making process, They reported an accuracy of 93.4% and DSC index of 0.94 in their study. Huang et al., [65] present results that predict ischemic tissue fate pixel-by-pixel based on multi-modal MRI data of acute stroke using a flexible support vector machine algorithm [65] . Nazari-Farsani et al., [27] proposes an identification of the ischemic stroke through SVM with Linear Kernel and cross validation folder with an accuracy of 73% with a private dataset of 192 patients scans, while Qiu et al., [151] with a private dataset of 1000 patients for the same task use only the Random Forest (RF) classifier and obtain an accuracy of 95%. The combination of the traditional classifier likes SVM and RF with CNN show better results, e.g. [32, 65, 143] report values of DSC between 0.80 and 0.86. Melingi and Vivekanand [131] reported that through combination of the Kernelized Fuzzy C-Means clustering and SVM they achieved an accuracy of 98.8% and sensitivity of 99%. A method for detecting the stroke presence or non-stroke presence using the SVM and feed-forward backpropagation neural networks classifiers, is presented in [15] . For extraction of the features of the segmentation of the stroke region a k-means clustering was used along with adaptive neuro fuzzy inference system (ANFIS) classifier, since the other two methods failed to detect the stroke region in low edges brain images, resulting in the accuracy and the precision of 99.8% and 97.3% respectively. The different developments of architectures in the DL models contribute to get better evaluation and results of segmentation, e.g. Kumar et al., [136] proposed a combination of U-Net and Fractal Networks, Fractal networks, are based on the repetitive generation of self-similar objects and ruling out residual connections [136, 166] . They This section presents a brief summary of some common problems found in the processing of ischemia and demyelinating disease images. The availability of large datasets is a major problem in medical imaging studies, and there are few datasets related to specific diseases [27] . The lack of datasets is a challenge since deep learning methods require a large amount of data for training, test and validation [27] . Another major problem is that even though algorithms for ischemic stroke segmentation in MRI scans have been (and are) intensively researched, but the reported results in general do not allow us to establish a comparative analysis due to the use of different databases (privates and public) with different validation schemes [29, 34] . The Ischemic Stroke Lesion Segmentation (ISLES) challenge, was designed to facilitate the development of tools for the segmentation of stroke lesions [20, 68, 142] . The Ischemic Stroke Lesion Segmentation (ISLES) group [20, 68] have a set of stroke images, but there is a need to enrich the dataset with clinical information, in order to get better performance with CNNs. Another problem with the datasets, is the need for accurately labeled data [37] , This lack of annotated data constitutes a major challenge for ML supervised algorithms [168] because the methods have to learn and train with limited annotated data which in most cases contain weak annotations (sparse annotations, noisy annotations, or only image level annotations) [144] . Therefore collecting image data in a structured and systematic way is imperative [79] due the large database required by the AI techniques to function efficiently. An example of good practice of health data (images and health information) is exemplified by the UK Biobank [169] , which has health data from half a million UK participants. The UK Biobank aims to create a large-scale biomedical database that can be accessed globally for public health research. However, the access depends on administrator approval and payment of a fee. Other difficulties that accompany the labeling of the images in a dataset include the lack of collaboration between clinical specialists and academics, patient privacy issues, and the most importantly the costly time-consuming task of manual labeling of data by clinicians [34] . With CNNs overfitting is a common problem due the small size of the training data [114] , and therefore it is important the increase of the size of training data and, one solution for this problem is the use of the technique of "data augmentation" which according to [170] , helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization, e.g. Tajbakhsh et al., in [144, 171] reported in their results that the sensitivity in a model improves 10% (from 62 to 72%) if the dataset is increased from a quarter to full size of the training dataset. Various methods of data augmentation of medical images are reviewed in [172] . However, in [141] it is suggested that cascaded CNN architectures are a practical solution for the problem of the limited annotated data, in that the proposed architecture tends to learn well from small sets of data [141] . An additional but no less important problem, is the availability of equipment for collecting the image data. Even though the MRI is better than CT for stroke diagnosis [173] there is also the fact that in some developing countries the availability of CT and MRI facilities is very limited and relatively expensive, in addition to lack of trained technical personnel and information [34] . Even in developed countries there are disparities in availability of equipment between urban and rural areas. These issues are discussed for example in a report published by the Organization of Economic Cooperation and Development (OECD) [174] . It is known that that the brain lesions have a high degree of variability [8, 64] , e.g., stroke lesions and tumors, and hence it is a hard and complex challenge to develop a system with great fidelity and precision. As an example, the lesion size and contrast affect the performance of the segmentation [18] . In the case of the WMHs and their association with a determined disease like the ischemic stroke, demyelinating disease or any other disorders, the set of features to describe their appearances and different locations [14] , plays a fundamental role for training with the minimum errors of any model. In medical image processing the computational cost is a fundamental factor, since the ML algorithms often require a large amount of data to ''learn'' to provide useful answers [100] and hence increased computational costs. Different studies [110, 113, 175] report that training neural networks which are efficient and make accurate predictions have a high computational cost (e.g. time, memory, and energy) [110] . This problem is often a limitation with the CNNs due to the high dimensionality of input data and the large number of training images required [113] . However graphical processing units (GPUs) have proven to be flexible and efficient hardware for ML purpose [100] . GPUs are highly specialized processors for image processing. The area of General purpose GPU (GPGPU) Computing is a growing area and is an essential part of many scientific computing applications. The basic architecture of a GPU differs a lot from a CPU. The GPU is Suzuki et al., [113, 178] propose the utilization of massive-training artificial neural network (MTANN) [179] instead of the CNNs because the CNN requires a huge number of training images (e.g., 1,000,000), while that the MTANN requires a small number of training images (e.g., 20) because of its simpler architecture. They note that with GPU implementation, an MTANN completes training in a few hours, whereas a deep CNN takes several days [113] , of course, currently this depends upon the task as well as the processor speed. It has been proposed that one can use small convolutional kernels in 3D CNNs [144] . This architecture seems to be more discriminative without increasing the computational cost and number of trainable parameters in relation to the task of identification [164] . The techniques of deep learning are going to play a major role in medical diagnosis in the future, and even with the high training cost, CNNs appear to have great potential and can serve as a preliminary step in the design and implementation of a CAD system [34] . However, brain lesions, and especially the WMHs have significant variants with respect to size, shape, intensity, and location, which makes their automatic and accurate segmentation challenging [159] ; e.g. in spite of the fact that stroke is considered to be easy to recognize and differentiate from other WMHs for experienced neuroradiologists, it could be a challenge and difficult task for general physicians, especially in rural areas or in developing countries where there are shortages of radiologists and neurologists and, for that reason it is important to employ computer-assisted methods as well as telemedicine [180] , in this sense, e.g. Mollura et al., [181] gives some strategies in order to get an effective and sustainable implementation of radiology in developing countries. Our research has noted diverse approaches in the detection differentiation of WHMs, especially with ischemic stroke and demyelinating disease like MS. Those include methods like support vector machine (SVM), neural networks, decision trees, or linear discrimination analysis. In the ISLES 2015 [20] and ISLES 2016 [68] competitions the best results were obtained for stroke lesion segmentation and outcome prediction using the classic machine learning models, specifically the Random Forest (RF); whereas in ISLES 2017 [68] the participants offered algorithms that use CNN, but the overall performance was not much different from ISLES 2016. However, the ISLES team state that despite this deep learning has the potential to influence clinical decision making for stroke lesion patients [68] . However, this is only in the research setting and has not been applied to a real clinical environment, in spite of development of many CAD systems [100] . To identify stroke, according to Huang et al., [65] the SVM method provides better prediction and quantitative metrics compared with the ANN. Also, they note the SVM provides accurate prediction with a small sample size [65, 182] , Feng et al., [142] indicate that the biggest barriers in applying deep learning techniques to medical data are the insufficiency of the large datasets that are needed to train DNNs [142] . Although various models trained with small datasets report good results (DSC values > 0.90) in their classifications or segmentations, see table 4 [15, 148, 152] , Davatzikos [183] recommends avoidance of methods trained with small datasets because of replicability and reproducibility issues [77, 183] . Therefore, it is important to have multidisciplinary groups [77, 98, 184] involving representatives from the clinical, academic and industrial communities in order to create efficient processes that can validate the algorithms and hence approve or refute recommendations made by software [77] . Related to this is that algorithmic development has to take into consideration that real life performance by clinicians is different from models. However other areas of medicine, for example ophthalmology has shown that certain classifiers approach clinician level performance. Of further importance is the development of explainable AI methods which have been applied to ophthalmology where correlations are made between areas of the image that the clinician uses to make decisions and the ones used by the algorithms to arrive at the result (i.e., the portions of the image which most heavily weighs the neural connexons) [71, [185] [186] [187] . Thus, the importance of involving actively clinical AI research, multidisciplinary communities, it is possible to pass the "valley of death" [100] namely the lack of resources and expertise often encountered in translational research. This will take into account the fact that currently deep learning is a black box [43] , where the inputs and outputs are known but the inner representations are not well understood. This is being alleviated by the development of explainable AI [72] . Even though there have been tremendous advances, there are only a few methods that are able to handle the vast range of radiological presentations of subtle disease states. There is a tremendous need for large annotated clinical data sets, a problem that can be (partially) solved by data augmentation and by methods of transfer learning [188, 189] used in the models principally with different CNNs architectures. Although it is very important to note that processing diseases or tasks in medical images are not the same as processing general pictures of say, dogs or cats, but it is possible uses a set of generic features already trained in CNNs for a specific task to transfer as features for input to classifiers focused on other medical imaging tasks. For examples in medical imaging see: [190] [191] [192] [193] . Finally it is important keep in mind the fact of the mentioned by [194] that like humans, the software is only as good as the data it is trained on. Therefore, it is important that research in medical image analysis and diagnosis must include both clinical and technical knowledge. . Conceptual Mindfact (Mentefacto conceptual) according to [47, 48] . This allows the keyword identification for a systemic search of the literature in the scientific databases. Tables Table 1 . Key Words used in the global semantic structure search ( ( ( magnetic* ) AND ( resonanc* ) AND ( imag* OR picture OR visualiz* ) ) OR mri OR mra ) Processing Brain ( algorithm* OR svm OR dwt OR kmeans OR pca OR cnn OR ann ) ) AND ( "deep learning" ) OR ( "neural networks" ) OR ( "machine learning" ) OR ( "convolutional neural network" ) OR ( "radiomics" ) Disease ( ( brain* OR cerebrum ) AND ( ( ischemic AND strok* ) OR ( demyelinating AND ( disease OR "brain lesions" ) ) ) ) TITLE-ABS-KEY ( ( ( ( magnetic* ) AND ( resonanc* ) AND ( imag* OR picture OR visualiz* ) ) OR mri OR mra ) AND ( ( brain* OR cerebrum ) AND ( ( ischemic AND strok* ) OR ( demyelinating AND ( disease OR "brain lesions" ) ) ) ) AND ( algorithm* OR svm OR dwt OR kmeans OR pca OR cnn OR ann ) ) AND ( "deep learning" ) OR ( "neural networks" ) OR ( "machine learning" ) OR ( "convolutional neural network" ) OR ( "radiomics" ) Table 2 . List of the ten most cited articles according to the normalization of the citations [51] . Also shows the central theme of research, the type of image and the methodology used in the processing. Neurological disorders: public health challenges Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review Magnetic resonance brain images algorithm to identify demyelinating and ischemic diseases Demyelinating and ischemic brain diseases: Detection algorithm through regular magnetic resonance images Neuroradiological evaluation of demyelinating disease Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer's Disease: A Survey Early identification of potentially salvageable tissue with MRI-based predictive algorithms after experimental ischemic stroke Encyclopedia of the Neurological Sciences Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders Magnetic Resonance Imaging (MRI) in Neurologic Disorders -Neurologic Disorders. MSD Manual Professional Edition n Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease Computer aided detection and diagnosis methodology for brain stroke using adaptive neuro fuzzy inference system classifier Demyelinating diseases Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal Automatic detection of ischemic stroke using higher order spectra features in brain MRI images SEGMENTATION and CLASSIFICATION of ISCHEMIC STROKE USING OPTIMIZED FEATURES in BRAIN MRI ISLES 2015 -A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI A Machine Learning Approach for Classifying Ischemic Stroke Onset Time from Imaging Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier Identifying Demyelinating and Ischemia brain diseases through magnetic resonance images processing Segmentation of multiple sclerosis lesions in MR images: A review Improved Diagnostic Process of Multiple Sclerosis Using Automated Detection and Selection Process in Magnetic Resonance Imaging Multiple sclerosis Automated segmentation of acute stroke lesions using a data-driven anomaly detection on diffusion weighted MRI Guidelines for the early management of adults with ischemic stroke: A guideline from the American heart association/American stroke association stroke council, clinical cardiology council, cardiovascular radiology and intervention council, and the atherosclerotic peripheral vascular disease and quality of care outcomes in research interdisciplinary working groups Ischemic Stroke Detection System with a Computer-Aided Diagnostic Ability Using an Unsupervised Feature Perception Enhancement Method A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation -With Application to Tumor and Stroke Chapter 10 -Classification of Myocardial Ischemia in Delayed Contrast Enhancement Using Machine Learning Classifiers for ischemic stroke lesion segmentation: A comparison study Neurological diseases Automatic Neuroimage Processing and Analysis in Stroke -A Systematic Review Machine learning in acute ischemic stroke neuroimaging Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks Ishemic Stroke Lesion Segmentation by Analyzing MRI Images Using Dilated and Transposed Convolutions in Convolutional Neural Networks Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning Evaluation of feature selection algorithms for classification in temporal lobe epilepsy based on MR images Radiomics: the process and the challenges Radiomics: the facts and the challenges of image analysis Structural neuroimaging as clinical predictor: A review of machine learning applications Medical Image Analysis using Convolutional Neural Networks: A Review Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges Five steps to conducting a systematic review Gesture-Based Children Computer Interaction for Inclusive Education: A Systematic Literature Review Methodology for systematic literature review applied to engineering and education University-industry cooperation: A systematic literature review and research agenda Visualizing Bibliometric Networks A unified approach to mapping and clustering of bibliometric networks Constructing bibliometric networks: A comparison between full and fractional counting Scopus -Document search | Signed in n Document search -Web of Science Core Collection n com | Science, health and medical journals, full text articles and books Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data White matter hyperintensity burden in acute stroke patients differs by ischemic stroke subtype Deep learning in medical image analysis: A third eye for doctors A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases How to normalize cooccurrence data? An analysis of some well-known similarity measures VOSviewer Manual n.d Quantitative prediction of acute ischemic tissue fate using support vector machine Shannon's entropy and watershed algorithm based technique to inspect ischemic stroke wound Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence Deep Learning for Retinal Analysis What is the Optimal Attribution Method for Explainable Ophthalmic Disease Classification? Explainable Deep Learning Models in Medical Image Analysis Ophthalmic diagnosis using deep learning with fundus images -A critical review Deep Learning for Ophthalmology using Optical Coherence Tomography Diagnosis of Retinal Diseases: New Results Using Deep Learning. Proceedings of the Congress of Applied and Educational Mathematics An overview of deep learning in medical imaging focusing on MRI Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology Machine Learning in Radiology: Applications Beyond Image Interpretation Deep into the Brain: Artificial Intelligence in Stroke Imaging Deep Learning Classification of Abnormalities in Brain MRI Images Using GLCM Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review Classification of MRI brain images using k-nearest neighbor and artificial neural network Ibn al-Haytham: Founder of Physiological Optics? Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review Random forest based classification of medical x-ray images using a genetic algorithm for feature selection Medical Image Recognition, Segmentation and Parsing -1st Edition n Random Forests Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization An efficient brain tumor detection methodology using Kmeans clustering algoriftnn MRI Brain Tumour Segmentation Using Hybrid Clustering and Classification by Back Propagation Algorithm Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation MRI brain tumor images classification using K-means clustering A guide to deep learning in healthcare A survey on deep learning in medical image analysis A gentle introduction to deep learning in medical image processing Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology Convolutional neural networks: an overview and application in radiology Learning representations by backpropagating errors Long Short-Term Memory Deep Learning in Medical Image Analysis Convolutional Neural Networks in Python. DataCamp Community Deep Learning in Neuroradiology Deep learning with convolutional neural network in radiology Deep Learning for Medical Image Processing: Overview, Challenges and the Future Opportunities and obstacles for deep learning in biology and medicine Machine learning applications in epilepsy A Machine Learning Approach for MRI Brain Tumor Classification Overview of deep learning in medical imaging Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges Deep Learning for Medical Image Analysis Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential Computer-Aided Detection and Diagnosis at the Start of the Third Millennium Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Artificial intelligence in radiology Radiomics: Images Are More than Pictures, They Are Data Extracting more information from medical images using advanced feature analysis A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS Repeatability and Reproducibility of Radiomic Features: A Systematic Review Unsupervised Deep Feature Learning for Deformable Registration of MR Brain Images Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning A large, open source dataset of stroke anatomical brain images and manual lesion segmentations Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure A crossbred approach for effective brain stroke lesion segmentation A deep supervised approach for ischemic lesion segmentation from multimodal MRI using Fully Convolutional Network 11 -Neutrosophic sets in dermoscopic medical image segmentation A comparison of automated lesion segmentation approaches for chronic stroke T1-weighted MRI data Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging CSNet: A new DeepNet framework for ischemic stroke lesion segmentation Brain lesion segmentation through image synthesis and outlier detection Measures of the Amount of Ecologic Association Between Species Lesion segmentation from multimodal MRI using random forest following ischemic stroke Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach Deep learning guided stroke management: a review of clinical applications Ischemic stroke lesion segmentation using stacked sparse autoencoder Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation Medical Image Computing and Computer-Assisted Intervention -MICCAI 2015 Classification of brain lesions from MRI images using a novel neural network Can we make a more efficient U-Net for blood vessel segmentation? Segmentation of Ischemic Stroke Lesion in Brain MRI Based on Social Group Optimization and Fuzzy-Tsallis Entropy Predicting final extent of ischemic infarction using artificial neural network analysis of multi-parametric MRI in patients with stroke Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning Machine Learning for Detecting Early Infarction in Acute Stroke with Non-Contrast-enhanced CT Robust unsupervised segmentation of infarct lesion from diffusion tensor MR images using multiscale statistical classification and partial volume voxel reclassification Level set evolution with locally linear classification for image segmentation Exploiting bilateral symmetry in brain lesion segmentation with reflective registration Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features Acute and subacute stroke lesion segmentation from multimodal MRI Better diffusion segmentation in acute ischemic stroke through automatic tree learning anomaly segmentation Machine learning approach to identify stroke within 4.5 hours Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation Automated tissue segmentation of MR brain images in the presence of white matter lesions White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation Deep Residual Learning for Image Recognition Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images Not-so-supervised: A survey of semisupervised, multi-instance, and transfer learning in medical image analysis Data Augmentation for Brain-Tumor Segmentation: A Review Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? A survey on Image Data Augmentation for Deep Learning Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison OECD Indicators. Medical technologies Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process GPUs reshape computing Using GPUs for machine learning algorithms Pixel-Based Machine Learning in Medical Imaging Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography Stroke Tissue Pattern Recognition Based on CT Texture Analysis White Paper Report of the RAD-AID Conference on International Radiology for Developing Countries: Identifying Challenges, Opportunities, and Strategies for Imaging Services in the Developing World Artificial neural network prediction of ischemic tissue fate in acute stroke imaging Machine learning in neuroimaging: Progress and challenges Current Applications and Future Impact of Machine Learning in Radiology Interpretation of deep learning using attributions: application to ophthalmic diagnosis Quantitative and Qualitative Evaluation of Explainable Deep Learning Methods for Ophthalmic Diagnosis Uncertainty aware and explainable diagnosis of retinal disease A survey of transfer learning Understanding the Mechanisms of Deep Transfer Learning for Medical Images Cross-domain diabetic retinopathy detection using deep learning Glaucoma diagnosis using transfer learning methods Rapid Classification of Glaucomatous Fundus Images using Transfer Learning Methods COVID-19 X-ray Image Classification Using Transfer Learning What Do These Terms Mean and How Will They Impact Health Care? History of artificial intelligence in medicine Is Artificial Intelligence Going to Replace Dermatologists? n The random subspace method for constructing decision forests SVM: Feature Selection and Kernels Noun Project: Free Icons & Stock Photos for Everything n An Introduction to Convolutional Neural Networks Computer-aided imaging analysis in acute ischemic stroke -background and clinical applications The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain NiftyNet: a deeplearning platform for medical imaging A large annotated medical image dataset for the development and evaluation of segmentation algorithms Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI Chapter 3 -Medical image diagnosis for disease detection: A deep learning approach CS231n Convolutional Neural Networks for Visual Recognition n Backpropagation Applied to Handwritten Zip Code Recognition ImageNet classification with deep convolutional neural networks Visualizing and Understanding Convolutional Networks Going Deeper with Convolutions Very Deep Convolutional Networks for Large-Scale Image Recognition Densely Connected Convolutional Networks. ArXiv:160806993 [Cs Squeeze-and-Excitation Networks NASNet -Neural Architecture Search Network (Image Classification). Medium You Only Look Once: Unified, Real-Time Object Detection A friendly Introduction to Siamese Networks Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation VL acknowledges the award of a DISCOVERY grant from the Natural Sciences and Author Table 3 . List of datasets, details, type (public or private) , web site and their reference dedicated to ischemia (stroke) and demyelinating diseases (MS), also are listed platforms where is possible find datasets for brain medical image processing.