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.
Highlights
There are estimated to be as many as a billion people worldwide [1] affected by peripheral and central neurological disorders [1,2]
There is much work to be done to develop accurate methods to get results comparable to those of specialists [43]. This critical review summarizes the literature on deep learning and machine learning techniques in the processing, segmentation, and detection of features of white matter hyperintensities (WMHs) found in ischemic and demyelinating diseases in brain magnetic resonance (MR) images
According to the second selection criterion, we found 38 documents to include in the analysis of this work that were related to and in agreement with the items described above
Summary
There are estimated to be as many as a billion people worldwide [1] affected by peripheral and central neurological disorders [1,2]. For identifying neurological disorders like stroke and demyelinating disease, the manual segmentation and delineation of anomalous brain tissue is the gold standard for lesion identification This method is very time consuming and specialist experience dependent [25,37], and because of these limitations, automatic detection of neurological disorders is necessary, even though it is a complex task because of data variability, e.g., in the case of ischemic stroke lesions, data variability could include the lesion shape and location, and factors like symptom onset, occlusion site, and patient differences [38]. There is much work to be done to develop accurate methods to get results comparable to those of specialists [43] This critical review summarizes the literature on deep learning and machine learning techniques in the processing, segmentation, and detection of features of WMHs found in ischemic and demyelinating diseases in brain MR images.
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