Automatic Neuroimage Processing and Analysis in Stroke—A Systematic Review

Sarmento, Róger Moura; Vasconcelos, Francisco Fábio Ximenes; Gupta, Deepak; Wu, Wanqing; Albuquerque, Victor Hugo C. de

doi:10.1109/rbme.2019.2934500

Cited by 30 publications

(31 citation statements)

References 83 publications

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“…There has been much research and development of CADx systems using different modalities of medical images. CAD is not a substitute for the specialist but can assist or be an adjunct to the specialist in the interpretation of the images [40]. 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 [123].…”

Section: Computer-aided Diagnosis In Medical Imaging (Cadx System)mentioning

confidence: 99%

“…There are several preprocessing steps necessary to reduce noise and artifacts in the medical images, which should be performed before the segmentation [40,166,167].…”

Section: Image Preprocessingmentioning

confidence: 99%

“…Another major problem is that even though algorithms for ischemic stroke segmentation in MRI scans have been (and are) intensively researched, the reported results in general do not allow us to establish a comparative analysis due to the use of different databases (private and public) with different validation schemes [35,40].…”

Section: The Datasetmentioning

confidence: 99%

“…In the past few years, there has been considerable research in the field of machine learning (ML) and deep learning (DL) to create automatic or semiautomatic systems, algorithms, and methods that allow detection of lesions in the brain, such as tumors, MS, stroke, glioma, AD, etc. [4,6,[8][9][10]26,28,30,36,[39][40][41][42][43][44][45][46][47][48]. Different studies demonstrate that deep learning algorithms can be successfully used for medical image retrieval, segmentation, computer-aided diagnosis, disease detection, and classification [49][50][51].…”

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: Computer-aided Diagnosis In Medical Imaging (Cadx System)mentioning

confidence: 99%

“…There are several preprocessing steps necessary to reduce noise and artifacts in the medical images, which should be performed before the segmentation [40,166,167].…”

Section: Image Preprocessingmentioning

confidence: 99%

Section: The Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MR Images, Brain Lesions, and Deep Learning

2021

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“… Our approach presents advantages in aiding the diagnosis of Parkinson’s disease patients in regions where medical healthcare may be limited. The results can also be analyzed via the Internet, allowing difficult to access areas to receive treatment with the same overall quality as the ones given in metropolitan areas, which is an important feature in the modern medical scenario of online monitoring [ 8 ]. Six new datasets were created from control and test groups, with 20 patients each, and assessed through our proposed device, these being available online ( ).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Sensory Pen for Aiding in the Diagnosis of Parkinson’s Disease from Dynamic Handwriting Analysis

Júnior

Delmiro

Magaia

et al. 2020

Sensors

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In this paper, we propose a pen device capable of detecting specific features from dynamic handwriting tests for aiding on automatic Parkinson’s disease identification. The method used in this work uses machine learning to compare the raw signals from different sensors in the device coupled to a pen and extract relevant information such as tremors and hand acceleration to diagnose the patient clinically. Additionally, the datasets composed of raw signals from healthy and Parkinson’s disease patients acquired here are made available to further contribute to research related to this topic.

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