Computerized evaluation method of white matter hyperintensities related to subcortical vascular dementia in brain MR images

2010 ◽  
Author(s):  
Hidetaka Arimura ◽  
Yasuo Kawata ◽  
Yasuo Yamashita ◽  
Taiki Magome ◽  
Masafumi Ohki ◽  
...  
Keyword(s):  
White Matter ◽  
Vascular Dementia ◽  
White Matter Hyperintensities ◽  
Evaluation Method ◽  
Mr Images ◽  
Subcortical Vascular Dementia ◽  
Brain Mr Images

scholarly journals Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images

2020 ◽  
Author(s):  
Vaanathi Sundaresan ◽  
Giovanna Zamboni ◽  
Peter M. Rothwell ◽  
Mark Jenkinson ◽  
Ludovica Griffanti
Keyword(s):  
White Matter ◽  
White Matter Hyperintensities ◽  
Training Data ◽  
Periventricular White Matter ◽  
Mr Images ◽  
Comparable Performance ◽  
Brain Mr Images ◽  
Reliable Quantification ◽  
Anatomical Information ◽  
Better Than

AbstractWhite matter hyperintensities (WMHs) have been associated with various cerebrovascular and neurodegenerative diseases. Reliable quantification of WMHs is essential for understanding their clinical impact in normal and pathological populations. Automated segmentation of WMHs is highly challenging due to heterogeneity in WMH characteristics between deep and periventricular white matter, presence of artefacts and differences in the pathology and demographics of populations. In this work, we propose an ensemble triplanar network that combines the predictions from three different planes of brain MR images to provide an accurate WMH segmentation. Also, the network uses anatomical information regarding WMH spatial distribution in loss functions for improving the efficiency of segmentation and to overcome the contrast variations between deep and periventricular WMHs. We evaluated our method on 5 datasets, of which 3 are part of a publicly available dataset (training data for MICCAI WMH Segmentation Challenge 2017 - MWSC 2017) consisting of subjects from three different cohorts. On evaluating our method separately in deep and periventricular regions, we observed robust and comparable performance in both regions. Our method performed better than most of the existing methods, including FSL BIANCA, and on par with the top ranking deep learning method of MWSC 2017.

Novel Automated Method for the Detection of White Matter Hyperintensities in Brain Multispectral MR Images

2020 ◽  
Vol 16 (5) ◽  
pp. 469-478
Author(s):  
Hsian-Min Chen ◽  
Clayton Chi-Chang Chen ◽  
Hsin Che Wang ◽  
Yung-Chieh Chang ◽  
Kuan-Jung Pan ◽  
...  
Keyword(s):  
White Matter ◽  
Anomaly Detection ◽  
White Matter Hyperintensities ◽  
Similarity Index ◽  
White Matter Lesions ◽  
Cerebral White Matter ◽  
Mr Images ◽  
Detection Algorithms ◽  
Automated Method ◽  
Brain Mr Images

Background: According to the Standards for Reporting Vascular Changes on Neuroimaging, White Matter Hyperintensities (WMHs) are cerebral white matter lesions that are characterized by abnormal tissues of variable sizes and appear hyperintense in T2-weighted Magnetic Resonance (MR) measurements without cavitation (i.e., their tissue signals differ from those of Cerebrospinal Fluid or CSF). Such abnormal tissue regions are typically observed in the MR images of brains of healthy older adults and are associated with a number of geriatric neurodegenerative diseases. Explanations of the exact causes and mechanisms of these diseases remain inconclusive. Moreover, WMHs are typically identified by visual assessment and manual examination, both of which require considerable time. This brings up a need of developing a method for detecting WMHs more objectively and enabling patients to be treated early. As a consequence, damages on nerve cells can be limited and the severity of patients’ conditions can be contained. Aims: This paper presents a computer-aided technique for automatically detecting and segmenting anomalies in MR images. Methods: The method has two steps: (1) a Band Expansion Process (BEP) to expand the dimensions of brain MR images nonlinearly and (2) anomaly detection algorithms to detect WMHs. Synthesized MR images provided by BrainWeb were used as benchmarks against which the detection performance of the algorithms was determined. Results: The most notable findings are as follows: Firstly, compared with the other anomaly detection algorithms and the Lesion Segmentation Tool (LST), BEP-anomaly detection is shown to be the most effective in detecting WMHs. Secondly, across all levels of background noise and inhomogeneity, the mean Similarity Index (SI) produced by our proposed algorithm is higher than that produced by LST, indicating that the algorithm is more effective than LST in segmenting WMHs from brain MR images. Conclusion: Experimental results demonstrated a significantly high accuracy of the BEP-K/R-RX method in detection of synthetic brain MS lesion data. In the meantime, it also effectively enhances the detection of brain lesions.

scholarly journals Gait and Equilibrium in Subcortical Vascular Dementia

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Rita Moretti ◽  
Paola Torre ◽  
Rodolfo M. Antonello ◽  
Francesca Esposito ◽  
Giuseppe Bellini
Keyword(s):  
White Matter ◽  
Vascular Dementia ◽  
Clinical Entity ◽  
Drug Intake ◽  
Men And Women ◽  
Imbalance Problem ◽  
Subcortical Vascular Dementia

Subcortical vascular dementia is a clinical entity, widespread, even challenging to diagnose and correctly treat. Patients with this diagnosis are old, frail, often with concomitant pathologies, and therefore, with many drugs in therapy. We tried to diagnose and follow up for three years more than 600 patients. Study subjects were men and women, not bedridden, aged 68–94 years, outpatients, recruited from June, 1st 2007 to June, 1st 2010. We examined them clinically, neurologically, with specific consideration on drug therapies. Our aim has been to define gait and imbalance problem, if eventually coexistent with the pathology of white matter and/or with the worsening of the deterioration. Drug intake interference has been detected and considered.

scholarly journals Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images

NeuroImage ◽  
2018 ◽  
Vol 183 ◽  
pp. 650-665 ◽  
Author(s):  
Hongwei Li ◽  
Gongfa Jiang ◽  
Jianguo Zhang ◽  
Ruixuan Wang ◽  
Zhaolei Wang ◽  
...  
Keyword(s):  
White Matter ◽  
White Matter Hyperintensities ◽  
Mr Images ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Network Ensembles

scholarly journals Peripheral (deep) but not periventricular MRI white matter hyperintensities are increased in clinical vascular dementia compared to Alzheimer's disease

2016 ◽  
Vol 6 (3) ◽  
Author(s):  
Charles D. Smith ◽  
Eleanor S. Johnson ◽  
Linda J. Van Eldik ◽  
Gregory A. Jicha ◽  
Frederick A. Schmitt ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Vascular Dementia ◽  
White Matter Hyperintensities

scholarly journals A mouse model of subcortical vascular dementia reflecting degeneration of cerebral white matter and microcirculation

2017 ◽  
Vol 39 (1) ◽  
pp. 44-57 ◽  
Author(s):  
Eek-Sung Lee ◽  
Jin-Hui Yoon ◽  
Jiye Choi ◽  
Faris R Andika ◽  
Taekwan Lee ◽  
...  
Keyword(s):  
Working Memory ◽  
White Matter ◽  
Recognition Memory ◽  
Mouse Model ◽  
Vascular Dementia ◽  
Spatial Working Memory ◽  
Cerebral Hypoperfusion ◽  
Cerebral Microcirculation ◽  
White Matter Damage ◽  
Subcortical Vascular Dementia

Subcortical vascular dementia(SVaD) is associated with white matter damage, lacunar infarction, and degeneration of cerebral microcirculation. Currently available mouse models can mimic only partial aspects of human SVaD features. Here, we combined bilateral common carotid artery stenosis (BCAS) with a hyperlipidaemia model in order to develop a mouse model of SVaD; 10- to 12-week-old apolipoprotein E (ApoE)-deficient or wild-type C57BL/6J mice were subjected to sham operation or chronic cerebral hypoperfusion with BCAS using micro-coils. Behavioural performance (locomotion, spatial working memory, and recognition memory), histopathological findings (white matter damage, microinfarctions, astrogliosis), and cerebral microcirculation (microvascular density and blood–brain barrier (BBB) integrity) were investigated. ApoE-deficient mice subjected to BCAS showed impaired locomotion, spatial working memory, and recognition memory. They also showed white matter damage, multiple microinfarctions, astrogliosis, reduction in microvascular density, and BBB breakdown. The combination of chronic cerebral hypoperfusion and ApoE deficiency induced cognitive decline and cerebrovascular pathology, including white matter damage, multiple microinfarctions, and degeneration of cerebral microcirculation. Together, these features are all compatible with those of patients with SVaD. Thus, the proposed animal model is plausible for investigating SVaD pathophysiology and for application in preclinical drug studies.

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