Identifying Demyelinating and Ischemia brain diseases through magnetic resonance images processing

Castillo, Darwin; Samaniego, René; Jiménez, Yuliana; Macas, Luis Alberto Cuenca; Vivanco, Oscar; Alvarez-Gomez, Juan M.; Rodríguez-Álvarez, M. J.

doi:10.1109/nss/mic42101.2019.9060022

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…There are three types of ischemic stroke according to the Bamford clinical classification system [24]: (1) partial anterior circulation syndrome (PACS), where the middle/anterior cerebral regions are affected; (2) lacunar anterior circulation syndrome (LACS), where the occlusion is present in vessels that provide blood to the deep-brain regions; and (3) total anterior circulation stroke (TACS), when middle/anterior cerebral regions are affected due to a massive brain stroke [24,25]. Ischemic stroke is a common cerebrovascular disease [1,26,27] and one of the principal causes of death and disability in low-and middle-income countries [1,4,6,7,[27][28][29]. In developed countries, brain ischemia is responsible for 75-80% of strokes, and 10-15% are attributed to a hemorrhagic brain stroke [4,25].…”

Section: Introductionmentioning

confidence: 99%

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MR Images, Brain Lesions, and Deep Learning

2021

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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.

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Section: Introductionmentioning

confidence: 99%

“…A demyelination disease is described as the loss of myelin with relative preservation of axons [8,22,29]. Love [22] notes that there are demyelinating diseases in which axonal degeneration occurs first and the degradation of myelin is secondary [7,22].…”

Section: Introductionmentioning

confidence: 99%

MR Images, Brain Lesions, and Deep Learning

2021

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“…There are three types of ischemic stroke according to the Bamford clinical classification system [18]: (1)Partial anterior circulation syndrome (PACS) where the middle/anterior cerebral regions are affected, (2) Lacunar syndrome (LACS) where the occlusion is present in the vessels that provide blood to the deep brain regions; and (3)Total anterior circulation stroke (TACS) when middle/anterior cerebral regions are affected due to a massive brain stroke [18,19]. Ischemic stroke is a common cerebrovascular disease [1,20,21] and one of the principal causes of death and disability in low and middle-income countries [1,4,5,11,[21][22][23]. In developed countries, brain ischemia is responsible for 75-80% of the strokes, and 10-15% are attributed to hemorrhagic brain stroke [11,19].…”

Section: Introductionmentioning

confidence: 99%

MRI Images, Brain Lesions and Deep Learning

Castillo¹,

Lakshminarayanan²,

Rodríguez-Álvarez³

2021

Preprint

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Medical brain image analysis is a necessary step in the Computers 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.

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“…For this reason an experienced radiologist is needed to accurately discern the changes of the hippocampal structures and abnormal signals. 7,13,14 In this context, artificial intelligence, namely Machine Learning/Deep Learning could help analyze and diagnose the MR brain images [15][16][17][18][19] as well as other parts of the human body. There is a need to detect and classify MTS images and the development of systems that could assess, detect and identify the changes of the hippocampal structures like the volume and intensity of signals.…”

Section: Introductionmentioning

confidence: 99%

MTS image analyzer: a software tool to identify mesial temporal sclerosis in MRI images

Castillo

Macas

Samaniego

et al. 2021

Applications of Digital Image Processing XLIV

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Epilepsy is a chronic neurological disorder that causes unprovoked and recurrent seizures which according to WHO affects approximately 50 million people worldwide. Functional magnetic resonance images (MRI) help to identify certain affected areas of the brain, namely, the gliosis and hippocampal volume loss. These losses cause complex epilepsy, and is known as hippocampal sclerosis or Mesial Temporal Sclerosis (MTS). This work presents the development of a Computer Aided Diagnosis CAD system software package) that can be used to identify the characteristics and patterns of MTS from brain magnetic resonance images. The image processing techniques involve texture analysis, statistical features, evaluation of the 3D Region of interest (ROI), and threshold analysis. The software allows the automatic evaluation of the degeneration of hippocampal structures, hippocampal volume and signal intensity. We will describe and demonstrate the software (which can currently be accessed on GitHub). It is expected that this tool will be useful in new neurology/radiology specialists and can serve as a secondary diagnosis. However, it is necessary to validate the software system qualitatively and quantitatively in order to get more effectiveness and efficiency in a real-world clinical application.

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Identifying Demyelinating and Ischemia brain diseases through magnetic resonance images processing

Cited by 7 publications

References 12 publications

MR Images, Brain Lesions, and Deep Learning

MR Images, Brain Lesions, and Deep Learning

MRI Images, Brain Lesions and Deep Learning

MTS image analyzer: a software tool to identify mesial temporal sclerosis in MRI images

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