scholarly journals Fully automatic brain segmentation using model-guided level sets and skeleton-based models

2013 ◽  
Author(s):  
Chunliang Wang ◽  
Chun Wang ◽  
Örjan Smedby
Keyword(s):  
Level Set ◽  
Gray Matter ◽  
Brain Segmentation ◽  
Segmentation Method ◽  
Implicit Model ◽  
Segmentation Methods ◽  
Cortical Gray Matter ◽  
Fully Automatic ◽  
Level Set Framework ◽  
The Brain

A fully automatic brain segmentation method is presented. First the skull is stripped using a model-based level set on T1-weighted inversion recovery images, then the brain ventricles and basal ganglia are segmented using the same method on T1-weighted images. The central white matter is segmented using a regular level set method but with high curvature regulation. To segment the cortical gray matter, a skeleton-based model is created by extracting the mid-surface of the gray matter from a preliminary segmentation using a threshold-based level set. An implicit model is then built by defining the thickness of the gray matter to be 2.7 mm. This model is incorporated into the level set framework and used to guide a second round more precise segmentation. Preliminary experiments show that the proposed method can provide relatively accurate results compared with the segmentation done by human observers. The processing time is considerably shorter than most conventional automatic brain segmentation methods.

scholarly journals MR Image Analysis of Neonatal Brain using Adaptive Mathematical Morphological Processing and Region based Segmentation

2019 ◽  
Vol 9 (1) ◽  
pp. 4127-4132
Keyword(s):  
Gray Matter ◽  
Right Hemisphere ◽  
Morphological Processing ◽  
Adult Brain ◽  
Size Difference ◽  
Mr Images ◽  
Neonatal Brain ◽  
Cortical Gray Matter ◽  
Manual Interaction ◽  
The Brain

In the study of evaluation of infant brain development, the segmentation of obtained MR images is an important step. When compared with the MR images of adult brain it is difficult to identify the different regions in infant brains with that of the methods used for the analysis of adult brains. This is due to the size difference of the brain and the differences in the properties of the brain tissues. So, for analyzing these MR images it requires manual interaction with the images resulting in the bias of the results. For this problem we propose another approach for the segmentation of neonatal brain MR images. This method doesn’t require any manual interaction and produces unbiased results. Our algorithm segments the different layers (right hemisphere, left hemisphere, cerebellum, brain stem) and the different tissues like sub cortical gray matter, Militated & un mylinated gray matter and cerebrospinal fluid, resulting in the better understanding of the development of different parts of the brain. Our algorithm can be used for the analysis of MR images of infant brains of age as less as 3to6 months.

scholarly journals Multimodal MR Brain Segmentation Using Bayesian-based Adaptive Mean-Shift (BAMS)

2013 ◽  
Author(s):  
Qaiser Mahmood ◽  
Mohammad Alipoor ◽  
Artur Chodorowski ◽  
Andrew Mehnert1 ◽  
Mikael Persson
Keyword(s):  
Mean Shift ◽  
Ground Truth ◽  
Segmentation Algorithm ◽  
Brain Segmentation ◽  
Mr Images ◽  
Unsupervised Segmentation ◽  
Fully Automatic ◽  
The Brain ◽  
Mr Brain ◽  
Dice Index

In this paper, we validate our proposed segmentation algorithm called Bayesian-based adaptive mean-shift (BAMS) on real mul-timodal MR images provided by the MRBrainS challenge. BAMS is a fully automatic unsupervised segmentation algorithm. It is based on the adaptive mean shift wherein the adaptive bandwidth of the kernel for each feature point is estimated using our proposed Bayesian approach [1]. BAMS is designed to segment the brain into three tissues; white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The performance of the algorithm is evaluated relative to the manual segmentation (ground truth). The results of our proposed algorithm show the average Dice index 0.8377±0.036 for the WM, 0.7637±0.038 for the GM and 0.6835 ±0.023 for the CSF.

scholarly journals Cardiovascular risks impact human brain N-acetylaspartate in regionally specific patterns

2019 ◽  
Vol 116 (50) ◽  
pp. 25243-25249
Author(s):  
Joshua Chiappelli ◽  
Laura M. Rowland ◽  
S. Andrea Wijtenburg ◽  
Hongji Chen ◽  
Andrew A. Maudsley ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Risk ◽  
White Matter ◽  
Cardiovascular Risk Factors ◽  
Gray Matter ◽  
Lipid Synthesis ◽  
Cardiovascular Risks ◽  
Myelin Lipid ◽  
Cortical Gray Matter ◽  
The Brain

Cardiovascular risk factors such as dyslipidemia and hypertension increase the risk for white matter pathology and cognitive decline. We hypothesize that white matter levels of N-acetylaspartate (NAA), a chemical involved in the metabolic pathway for myelin lipid synthesis, could serve as a biomarker that tracks the influence of cardiovascular risk factors on white matter prior to emergence of clinical changes. To test this, we measured levels of NAA across white matter and gray matter in the brain using echo planar spectroscopic imaging (EPSI) in 163 individuals and examined the relationship of regional NAA levels and cardiovascular risk factors as indexed by the Framingham Cardiovascular Risk Score (FCVRS). NAA was strongly and negatively correlated with FCVRS across the brain, but, after accounting for age and sex, the association was found primarily in white matter regions, with additional effects found in the thalamus, hippocampus, and cingulate gyrus. FCVRS was also negatively correlated with creatine levels, again primarily in white matter. The results suggest that cardiovascular risks are related to neurochemistry with a predominantly white matter pattern and some subcortical and cortical gray matter involvement. NAA mapping of the brain may provide early surveillance for the potential subclinical impact of cardiovascular and metabolic risk factors on the brain.

T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance

2017 ◽  
Author(s):  
John D Lewis ◽  
Alan C Evans ◽  
Jussi Tohka
Keyword(s):  
Brain Development ◽  
Cognitive Performance ◽  
Gray Matter ◽  
Brain Regions ◽  
Brain Maturation ◽  
Chronological Age ◽  
Brain Images ◽  
Cortical Gray Matter ◽  
The Brain ◽  
Age Prediction

The maturational schedule of human brain development appears to be narrowly confined. The chronological age of an individual can be predicted from brain images with considerable accuracy, and deviation from the typical pattern of brain maturation has been related to cognitive performance. Methods using multi-modal data, or complex measures derived from voxels throughout the brain have shown the greatest accuracy, but are difficult to interpret in terms of the biology. Measures based on the cortical surface(s) have yielded less accurate predictions, suggesting that perhaps developmental changes related to cortical gray matter are not strongly related to chronological age, and that perhaps development is more strongly related to changes in subcortical regions or in deep white matter. We show that a simple metric based on the white/gray contrast at the inner border of the cortical gray-matter is a comparably good predictor of chronological age, and our usage of an elastic net penalized linear regression model reveals the brain regions which contribute most to age-prediction. We demonstrate this in two large datasets: the NIH Pediatric Data, with 832 scans of typically developing children, adolescents, and young adults; and the Pediatric Imaging, Neurocognition, and Genetics data, with 760 scans of individuals in a similar age-range. Moreover, we show that the residuals of age-prediction based on this white/gray contrast metric are more strongly related to IQ than are those from cortical thickness, suggesting that this metric is more sensitive to aspects of brain development that reflect cognitive performance.

scholarly journals Multiple Sclerosis Lesion Segmentation Using Statistical and Topological Atlases

2008 ◽  
Author(s):  
Navid Shiee ◽  
Pierre-Louis Bazin ◽  
Dzung L. Pham
Keyword(s):  
Multiple Sclerosis ◽  
Magnetic Resonance Images ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation ◽  
Brain Images ◽  
Analysis Techniques ◽  
Segmentation Methods ◽  
Fully Automatic ◽  
The Brain ◽  
Statistical Atlas

This paper presents a new fully automatic method for segmentation of brain images that possess multiple sclerosis (MS) lesions. Multichannel magnetic resonance images are used to delineate multiple sclerosis lesions while segmenting the brain into its major structures. The method is an atlas based segmentation technique employing a topological atlas as well as a statistical atlas. An advantage of this approach is that all segmented structures are topologically constrained, thereby allowing subsequent processing with cortical unfolding or diffeomorphic shape analysis techniques. Validation on data from two studies demonstrates that the method has an accuracy comparable with other MS lesion segmentation methods, while simultaneously segmenting the whole brain.

scholarly journals Statistical Characterization of Human Brain Deformation During Mild Angular Acceleration Measured In Vivo by Tagged Magnetic Resonance Imaging

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Deva D. Chan ◽  
Andrew K. Knutsen ◽  
Yuan-Chiao Lu ◽  
Sarah H. Yang ◽  
Elizabeth Magrath ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Gray Matter ◽  
Tissue Type ◽  
Resonance Imaging ◽  
Brain Deformation ◽  
Cortical Gray Matter ◽  
Tagged Magnetic Resonance Imaging ◽  
The Brain

Understanding of in vivo brain biomechanical behavior is critical in the study of traumatic brain injury (TBI) mechanisms and prevention. Using tagged magnetic resonance imaging, we measured spatiotemporal brain deformations in 34 healthy human volunteers under mild angular accelerations of the head. Two-dimensional (2D) Lagrangian strains were examined throughout the brain in each subject. Strain metrics peaked shortly after contact with a padded stop, corresponding to the inertial response of the brain after head deceleration. Maximum shear strain of at least 3% was experienced at peak deformation by an area fraction (median±standard error) of 23.5±1.8% of cortical gray matter, 15.9±1.4% of white matter, and 4.0±1.5% of deep gray matter. Cortical gray matter strains were greater in the temporal cortex on the side of the initial contact with the padded stop and also in the contralateral temporal, frontal, and parietal cortex. These tissue-level deformations from a population of healthy volunteers provide the first in vivo measurements of full-volume brain deformation in response to known kinematics. Although strains differed in different tissue type and cortical lobes, no significant differences between male and female head accelerations or strain metrics were found. These cumulative results highlight important kinematic features of the brain's mechanical response and can be used to facilitate the evaluation of computational simulations of TBI.

scholarly journals Increased HLA-DR expression and cortical demyelination in MS links with HLA-DR15

2019 ◽  
Vol 7 (2) ◽  
pp. e656 ◽  
Author(s):  
Lukas Simon Enz ◽  
Thomas Zeis ◽  
Daniela Schmid ◽  
Florian Geier ◽  
Franziska van der Meer ◽  
...  
Keyword(s):  
Human Brain ◽  
Gray Matter ◽  
Immunohistochemical Analysis ◽  
Bimodal Distribution ◽  
Lesion Size ◽  
Cortical Lesion ◽  
Hla Dr ◽  
Cortical Gray Matter ◽  
Autopsy Tissues ◽  
The Brain

ObjectiveTo investigate molecular changes in multiple sclerosis (MS) normal-appearing cortical gray matter (NAGM).MethodsWe performed a whole-genome gene expression microarray analysis of human brain autopsy tissues from 64 MS NAGM samples and 42 control gray matter samples. We further examined our cases by HLA genotyping and performed immunohistochemical and immunofluorescent analysis of all human brain tissues.ResultsHLA-DRB1 is the transcript with highest expression in MS NAGM with a bimodal distribution among the examined cases. Genotyping revealed that every case with the MS-associated HLA-DR15 haplotype also shows high HLA-DRB1 expression and also of the tightly linked HLA-DRB5 allele. Quantitative immunohistochemical analysis confirmed the higher expression of HLA-DRB1 in HLA-DRB1*15:01 cases at the protein level. Analysis of gray matter lesion size revealed a significant increase of cortical lesion size in cases with high HLA-DRB1 expression.ConclusionsOur data indicate that increased HLA-DRB1 and -DRB5 expression in the brain of patients with MS may be an important factor in how the HLA-DR15 haplotype contributes to MS pathomechanisms in the target organ.

scholarly journals A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images

2018 ◽  
Vol 2018 ◽  
pp. 1-26 ◽  
Author(s):  
Zhou Zheng ◽  
Xuechang Zhang ◽  
Huafei Xu ◽  
Wang Liang ◽  
Siming Zheng ◽  
...  
Keyword(s):  
Level Set ◽  
Liver Tumor ◽  
Region Growing ◽  
Binary Image ◽  
Tumor Segmentation ◽  
Liver Segmentation ◽  
Segmentation Method ◽  
Liver Tumor Segmentation ◽  
Public Datasets ◽  
Level Set Framework

Accurate and reliable segmentation of liver tissue and liver tumor is essential for the follow-up of hepatic diagnosis. In this paper, we present a method for liver segmentation and a method for liver tumor segmentation. The two methods are grounded on a novel unified level set method (LSM), which incorporates both region information and edge information to evolve the contour. This level set framework is more resistant to edge leakage than the single-information driven LSMs for liver segmentation and surpasses many other models for liver tumor segmentation. Specifically, for liver segmentation, a hybrid image preprocessing scheme is used first to convert an input CT image into a binary image. Then with manual setting of a few seed points on the obtained binary image, the following region-growing is performed to extract a rough liver region with no leakage. The unified LSM is proposed at last to refine the segmentation result. For liver tumor segmentation, a local intensity clustering based LSM coupled with hidden Markov random field and expectation-maximization (HMRF-EM) algorithm is applied to construct an enhanced edge indicator for the unified LSM. With this development, expected segmentation results can be obtained via the unified LSM, even for complex tumors. The two methods were evaluated with various datasets containing a local hospital dataset, the public datasets SLIVER07, 3Dircadb, and MIDAS via five measures. The proposed liver segmentation method outperformed other previous semiautomatic methods on the SLIVER07 dataset and required less interaction. The proposed liver tumor segmentation method was also competitive with other state-of-the-art methods in both accuracy and efficiency on the 3Dircadb database. Our methods are evaluated to be accurate and efficient, which allows their adoptions in clinical practice.

scholarly journals Mediterranean versus Western diet effects on cerebral cortical thickness and volume in cynomolgus monkeys

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 489-490
Author(s):  
Brett Frye ◽  
Suzanne Craft ◽  
Thomas Register ◽  
Jeongchul Kim ◽  
Christopher Whitlow ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Gray Matter ◽  
Repeated Measures ◽  
Structural Changes ◽  
Diet Group ◽  
Western Diet ◽  
Brain Anatomy ◽  
Total Brain Volume ◽  
Cortical Gray Matter ◽  
The Brain

Abstract Diet may influence the risk for cognitive decline and neurodegenerative disorders, including Alzheimer’s disease (AD), but these relationships are difficult to study in humans. Cynomolgus macaques (Macaca fascicularis) are appropriate models for investigations of diet effects on the brain because, like humans, they are omnivorous, have complex central nervous systems, are susceptible to diet-induced diseases, and accumulate amyloid and tauopathies with age. Using structural magnetic resonance imaging, we examined diet effects on brain anatomy by measuring thickness and volume of several areas relevant to AD in 38 middle-aged females, at baseline and after Mediterranean or Western diet consumption for 36 months (equivalent to a 9-year follow-up in humans). Using repeated measures analysis, cortical thicknesses generally increased in the Western diet group. Western diets also resulted in increases in total brain volume and cortical gray matter and decreases in cerebrospinal fluid, white matter, and deep gray matter (striatum and thalamus) (all p’s≤0.05). In contrast, thicknesses and volumes generally remained unchanged in animals consuming Mediterranean diets. Taken together, these findings demonstrate that Western diets induce widespread structural shifts which may increase risk of cognitive decline and neuropathology, whereas Mediterranean diets may exert a stabilizing influence on the brain. This study provides important insights about the significance of diet on brain structure and lays the groundwork for future investigations to uncover the molecular underpinnings of diet-induced changes in the brain. Mediterranean diet may protect against structural changes in brain that occur with age in those consuming a Western diet.

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