Segmentation of MRI brain scans using non-uniform partial volume densities

We present an algorithm that provides a partial volume segmentation of a T1-weighted image of the brain into gray matter, white matter and cerebrospinal fluid. The algorithm incorporates a non-uniform partial volume density that takes the curved nature of the cortex into account. The pure gray and white matter intensities are estimated from the image, using scanner noise and cortical partial volume effects. Expected tissue fractions are subsequently computed in each voxel. The algorithm has been tested for reliability, correct estimation of the pure tissue intensities on both real (repeated) MRI data and on simulated (brain) images. Intra-class correlation coefficients (ICCs) were above 0.93 for all volumes of the three tissue types for repeated scans from the same scanner, as well as for scans with different voxel sizes from different scanners with different field strengths. The implementation of our non-uniform partial volume density provided more reliable volumes and tissue fractions, compared to a uniform partial volume density. Applying the algorithm to simulated images showed that the pure tissue intensities were estimated accurately. Variations in cortical thickness did not influence the accuracy of the volume estimates, which is a valuable property when studying (possible) group differences. In conclusion, we have presented a new partial volume segmentation algorithm that allows for comparisons over scanners and voxel sizes.     

Automatic segmentation of MR images of the developing newborn brain

This paper describes an automatic tissue segmentation method for newborn brains from magnetic resonance images (MRI). The analysis and study of newborn brain MRI is of great interest due to its potential for studying early growth patterns and morphological changes in neurodevelopmental disorders. Automatic segmentation of newborn MRI is a challenging task mainly due to the low intensity contrast and the growth process of the white matter tissue. Newborn white matter tissue undergoes a rapid myelination process, where the nerves are covered in myelin sheathes. It is necessary to identify the white matter tissue as myelinated or non-myelinated regions. The degree of myelination is a fractional voxel property that represents regional changes of white matter as a function of age. Our method makes use of a registered probabilistic brain atlas. The method first uses robust graph clustering and parameter estimation to find the initial intensity distributions. The distribution estimates are then used together with the spatial priors to perform bias correction. Finally, the method refines the segmentation using training sample pruning and non-parametric kernel density estimation. Our results demonstrate that the method is able to segment the brain tissue and identify myelinated and non-myelinated white matter regions.     

Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification

Accurate reconstruction of the inner and outer cortical surfaces of the human cerebrum is a critical objective for a wide variety of neuroimaging analysis purposes, including visualization, morphometry, and brain mapping. The Anatomic Segmentation using Proximity (ASP) algorithm, previously developed by our group, provides a topology-preserving cortical surface deformation method that has been extensively used for the aforementioned purposes. However, constraints in the algorithm to ensure topology preservation occasionally produce incorrect thickness measurements due to a restriction in the range of allowable distances between the gray and white matter surfaces. This problem is particularly prominent in pediatric brain images with tightly folded gyri. This paper presents a novel method for improving the conventional ASP algorithm by making use of partial volume information through probabilistic classification in order to allow for topology preservation across a less restricted range of cortical thickness values. The new algorithm also corrects the classification of the insular cortex by masking out subcortical tissues. For 70 pediatric brains, validation experiments for the modified algorithm, Constrained Laplacian ASP (CLASP), were performed by three methods: (i) volume matching between surface-masked gray matter (GM) and conventional tissue-classified GM, (ii) surface matching between simulated and CLASP-extracted surfaces, and (iii) repeatability of the surface reconstruction among 16 MRI scans of the same subject. In the volume-based evaluation, the volume enclosed by the CLASP WM and GM surfaces matched the classified GM volume 13% more accurately than using conventional ASP. In the surface-based evaluation, using synthesized thick cortex, the average difference between simulated and extracted surfaces was 4.6 +/- 1.4 mm for conventional ASP and 0.5 +/- 0.4 mm for CLASP. In a repeatability study, CLASP produced a 30% lower RMS error for the GM surface and a 8% lower RMS error for the WM surface compared with ASP.     

Brain segmentation and the generation of cortical surfaces.

This paper describes methods for white matter segmentation in brain images and the generation of cortical surfaces from the segmentations. We have developed a system that allows a user to start with a brain volume, obtained by modalities such as MRI or cryosection, and constructs a complete digital representation of the cortical surface. The methodology consists of three basic components: local parametric modeling and Bayesian segmentation; surface generation and local quadratic coordinate fitting; and surface editing. Segmentations are computed by parametrically fitting known density functions to the histogram of the image using the expectation maximization algorithm [DLR77]. The parametric fits are obtained locally rather than globally over the whole volume to overcome local variations in gray levels. To represent the boundary of the gray and white matter we use triangulated meshes generated using isosurface generation algorithms [GH95]. A complete system of local parametric quadratic charts [JWM+95] is superimposed on the triangulated graph to facilitate smoothing and geodesic curve tracking. Algorithms for surface editing include extraction of the largest closed surface. Results for several macaque brains are presented comparing automated and hand surface generation.     

The impact of skull-stripping and radio-frequency bias correction on grey-matter segmentation for voxel-based morphometry

This study evaluates the application of (i) skull-stripping methods (hybrid watershed algorithm (HWA), brain surface extractor (BSE) and brain-extraction tool (BET2)) and (ii) bias correction algorithms (nonparametric nonuniform intensity normalisation (N3), bias field corrector (BFC) and FMRIB's automated segmentation tool (FAST)) as pre-processing pipelines for the technique of voxel-based morphometry (VBM) using statistical parametric mapping v.5 (SPM5). The pipelines were evaluated using a BrainWeb phantom, and those that performed consistently were further assessed using artificial-lesion masks applied to 10 healthy controls compared to the original unlesioned scans, and finally, 20 Alzheimer's disease (AD) patients versus 23 controls. In each case, pipelines were compared to each other and to those from default SPM5 methodology. The BET2+N3 pipeline was found to produce the least miswarping to template induced by real abnormalities, and performed consistently better than the other methods for the above experiments. Occasionally, the clusters of significant differences located close to the boundary were dragged out of the glass-brain projections -- this could be corrected by adding background noise to low-probability voxels in the grey matter segments. This method was confirmed in a one-dimensional simulation and was preferable to threshold and explicit (simple) masking which excluded true abnormalities.     

Skull-stripping magnetic resonance brain images using a model-based level set

The segmentation of brain tissue from nonbrain tissue in magnetic resonance (MR) images, commonly referred to as skull stripping, is an important image processing step in many neuroimage studies. A new mathematical algorithm, a model-based level set (MLS), was developed for controlling the evolution of the zero level curve that is implicitly embedded in the level set function. The evolution of the curve was controlled using two terms in the level set equation, whose values represented the forces that determined the speed of the evolving curve. The first force was derived from the mean curvature of the curve, and the second was designed to model the intensity characteristics of the cortex in MR images. The combination of these forces in a level set framework pushed or pulled the curve toward the brain surface. Quantitative evaluation of the MLS algorithm was performed by comparing the results of the MLS algorithm to those obtained using expert segmentation in 29 sets of pediatric brain MR images and 20 sets of young adult MR images. Another 48 sets of elderly adult MR images were used for qualitatively evaluating the algorithm. The MLS algorithm was also compared to two existing methods, the brain extraction tool (BET) and the brain surface extractor (BSE), using the data from the Internet brain segmentation repository (IBSR). The MLS algorithm provides robust skull-stripping results, making it a promising tool for use in large, multi-institutional, population-based neuroimaging studies.     

Deformation-based morphometry of brain changes in alcohol dependence and abstinence

Brain atrophy associated with chronic alcohol consumption is partially reversible after cessation of drinking. Recovering alcoholics (RA, 45+/-8 years) were studied with MRI within 1 week of entering treatment, with follow-up at 8 months. Light drinkers (LD) were studied with MRI twice 1 year apart. For each participant, deformation maps of baseline structure and longitudinal size changes between baseline and follow-up scans were created using nonlinear registration techniques. ANCOVA assessed group differences and regression methods examined relationships between deformation maps and measures of drinking severity or baseline atrophy. At baseline, RA showed significant atrophy in the frontal and temporal lobes. Longitudinally, abstainers recovered tissue volumes significantly faster than LD in parietal and frontal lobes. When comparing abstainers to relapsers, additional regions with significantly greater recovery in abstainers were temporal lobes, thalamus, brainstem, cerebellum, corpus callosum, anterior cingulate, insula, and subcortical white matter. Gray matter volume at baseline predicted volume recovery during abstinence better than white matter. Drinking severity was not significantly related to brain structural changes assessed with this method. Longitudinally, deformation-based morphometry confirmed tissue recovery in RAs who maintain long-term sobriety. Abstinence-associated tissue volume gains are significant in focal parts of the fronto-ponto-cerebellar circuit that is adversely affected by heavy drinking.     

Delayed myelination in children with developmental delay detected by volumetric MRI

Delayed acquisition of developmental motor and cognitive milestones is a common clinical expression of many etiological processes. Imaging exams of developmentally delayed children often show no structural brain alterations despite suspicion of brain maturation delay. MRI studies increasingly suggest that white matter myelination finely reflects the progression in functional brain maturation. In this volumetric MRI study, we sought to evaluate whether developmental delay in children with normal conventional MRI exams is associated with reduced myelinated white matter. A total of 100 children (mean age, 4.4 years) with developmental delay and 50 normally developing age-matched control children underwent 3-D MRI to measure the volume of myelinated white matter. Patients showed a significant reduction in the relative content of myelinated white matter (accounting for 19.8% of brain volume in patients and 21.4% in control subjects, P = 0.005). The observed difference was equivalent to a 3.2-year myelination delay. Although the whole hemispheres were invariably symmetrical, the volume of myelinated white matter was asymmetrical in 30% of patients and 10% of control subjects (P = 0.006). We conclude that volumetric assessment of white matter may reveal a reduction in brain myelination beyond early childhood in developmentally delayed children showing normal brain appearance. This finding further emphasizes the view of white matter myelination as an indicator of functional brain maturation.     

Multiple indicators of age-related differences in cerebral white matter and the modifying effects of hypertension

We investigated differences associated with age and hypertension, a common risk factor for vascular disease, in three aspects of white matter integrity — gross regional volumes of the white matter, volume of the white matter hyperintensities (WMH) and diffusion properties. We acquired MRI scans on 93 adult volunteers (age 50–77 years; 36 with diagnosis of hypertension or elevated blood pressure), and obtained all measures in seven brain regions: frontal, temporal, parietal and occipital white matter, and the genu, body and splenium of the corpus callosum. The results demonstrated robust age-related differences in diffusion-based indices of cerebral white matter integrity and age-related increase in the WMH volume, but no age differences in the gross regional volumes of the white matter. Hypertension was associated with decline in fractional anisotropy, and exacerbated age differences in fractional anisotropy more than those in the volume of WMH. These findings indicate that of all examined measures, diffusion-based indices of white matter integrity may be the most sensitive indicators of global and regional declines and vascular damage in the aging brain.     

Magnetic resonance image tissue classification using a partial volume model

We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average kappa indices of kappa = 0.746 +/- 0.114 for gray matter (GM) and kappa = 0.798 +/- 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average kappa indices kappa = 0.893 +/- 0.041 for GM and kappa = 0.928 +/- 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute's BrainWeb phantom.     

Neuro-Fuzzy Approach Toward Segmentation of Brain MRI Based on Intensity and Spatial Distribution

Segmentation in image processing finds immense application in various areas. Image processing techniques can be used in medical applications for various diagnoses. In this article, we attempt to apply segmentation techniques to the brain images. Segmentation of brain magnetic resonance images (MRI) can be used to identify various neural disorders. We can segment abnormal tissues from the MRI, which and can be used for early detection of brain tumors. The segmentation, when applied to MRI, helps in extracting the different brain tissues such as white matter, gray matter and cerebrospinal fluid. Segmentation of these tissues helps in determining the volume of these tissues in the three-dimensional brain MRI. The study of volume changes helps in analyzing many neural disorders such as epilepsy and Alzheimer disease. We have proposed a hybrid method combining the classical Fuzzy C Means algorithm with neural network for segmentation.     

Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET

Magnetic resonance imaging (MRI)-guided partial volume effect correction (PVC) in brain positron emission tomography (PET) is now a well-established approach to compensate the large bias in the estimate of regional radioactivity concentration, especially for small structures. The accuracy of the algorithms developed so far is, however, largely dependent on the performance of segmentation methods partitioning MRI brain data into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of three brain MRI segmentation algorithms using simulated and clinical brain MR data was performed, and subsequently their impact on PVC in 18F-FDG and 18F-DOPA brain PET imaging was assessed. Two algorithms, the first is bundled in the Statistical Parametric Mapping (SPM2) package while the other is the Expectation Maximization Segmentation (EMS) algorithm, incorporate a priori probability images derived from MR images of a large number of subjects. The third, here referred to as the HBSA algorithm, is a histogram-based segmentation algorithm incorporating an Expectation Maximization approach to model a four-Gaussian mixture for both global and local histograms. Simulated under different combinations of noise and intensity non-uniformity, MR brain phantoms with known true volumes for the different brain classes were generated. The algorithms' performance was checked by calculating the kappa index assessing similarities with the "ground truth" as well as multiclass type I and type II errors including misclassification rates. The impact of image segmentation algorithms on PVC was then quantified using clinical data. The segmented tissues of patients' brain MRI were given as input to the region of interest (RoI)-based geometric transfer matrix (GTM) PVC algorithm, and quantitative comparisons were made. The results of digital MRI phantom studies suggest that the use of HBSA produces the best performance for WM classification. For GM classification, it is suggested to use the EMS. Segmentation performed on clinical MRI data show quite substantial differences, especially when lesions are present. For the particular case of PVC, SPM2 and EMS algorithms show very similar results and may be used interchangeably. The use of HBSA is not recommended for PVC. The partial volume corrected activities in some regions of the brain show quite large relative differences when performing paired analysis on 2 algorithms, implying a careful choice of the segmentation algorithm for GTM-based PVC.     

The topography of white matter hyperintensities on brain MRI in healthy 60- to 64-year-old individuals

We report the topography of brain white matter hyperintensities (WMHs) on T2-weighted fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging in 477 healthy subjects aged 60-64 years selected randomly from the community. WMHs were delineated by using a computer algorithm. We found that all subjects had periventricular WMHs and 96.6% subjects also had deep WMHs. The mean volume of WMHs was 4.9 ml, comprising 0.83% of the white matter, of which 1.2 ml was severe in intensity. The deep WMHs were distributed throughout the cerebral hemispheres, with the occipital and frontal white matter bearing the greatest burden. The territory of the lenticulostriate arteries had the greatest WMHs. A white matter region of 4 mm adjacent to the cortex was not affected by hyperintensities. The mean (SD) number of discrete WMHs was 19.6 (7.1) per subject, of which 6.1 (4.4) were severe in intensity. Nearly half (48.6%) of the subjects had at least one large WMH (>12 mm diameter) and one eighth (12.5%) of the subjects had at least one large WMH that appeared to be severe in MRI. The overall load of WMHs was similar in men and women, but the latter had a higher proportion of their white matter so affected. This study provides the first detailed topographic analysis of WMHs in a large representative middle-aged sample, emphasizes their high prevalence in mid-adult life and raises issues about their etiology and significance.     

Reconstruction of fetal brain MRI with intensity matching and complete outlier removal

We propose a method for the reconstruction of volumetric fetal MRI from 2D slices, comprising super-resolution reconstruction of the volume interleaved with slice-to-volume registration to correct for the motion. The method incorporates novel intensity matching of acquired 2D slices and robust statistics which completely excludes identified misregistered or corrupted voxels and slices. The reconstruction method is applied to motion-corrupted data simulated from MRI of a preterm neonate, as well as 10 clinically acquired thick-slice fetal MRI scans and three scan-sequence optimized thin-slice fetal datasets. The proposed method produced high quality reconstruction results from all the datasets to which it was applied. Quantitative analysis performed on simulated and clinical data shows that both intensity matching and robust statistics result in statistically significant improvement of super-resolution reconstruction. The proposed novel EM-based robust statistics also improves the reconstruction when compared to previously proposed Huber robust statistics. The best results are obtained when thin-slice data and the correct approximation of the point spread function is used. This paper addresses the need for a comprehensive reconstruction algorithm of 3D fetal MRI, so far lacking in the scientific literature.     

Estradiol treatment altered anticholinergic-related brain activation during working memory in postmenopausal women

Diffusion MRI is used extensively to investigate changes in white matter microstructure related to brain development and pathology. Ageing, however, is also associated with significant white and grey matter loss which in turn can lead to cerebrospinal fluid (CSF) based partial volume artefacts in diffusion MRI metrics. This is especially problematic in regions prone to CSF contamination, such as the fornix and the genu of corpus callosum, structures that pass through or close to the ventricles respectively. The aim of this study was to model the effects of CSF contamination on diffusion MRI metrics, and to evaluate different post-acquisition strategies to correct for CSF-contamination: Controlling for whole brain volume and correcting on a voxel-wise basis using the Free Water Elimination (FWE) approach. Using the fornix as an exemplar of a structure prone to CSF-contamination, corrections were applied to tract-specific and voxel-based [tract based spatial statistics (TBSS)] analyses of empirical DT-MRI data from 39 older adults (53-93 years of age). In addition to significant age-related decreases in whole brain volume and fornix tissue volume fraction, age was also associated with a reduction in mean fractional anisotropy and increase in diffusivity metrics in the fornix. The experimental data agreed with the simulations in that diffusivity metrics (mean diffusivity, axial and radial diffusivity) were more prone to partial volume CSF-contamination errors than fractional anisotropy. After FWE-based voxel-by-voxel partial volume corrections, the significant positive correlations between age and diffusivity metrics, in particular with axial diffusivity, disappeared whereas the correlation with anisotropy remained. In contrast, correcting for whole brain volume had little effect in removing these spurious correlations. Our study highlights the importance of correcting for CSF-contamination partial volume effects in the structures of interest on a voxel-by-voxel basis prior to drawing inferences about underlying changes in white matter structures and have implications for the interpretation of many recent diffusion MRI results in ageing and disease.     

模糊K-均值聚类算法及其在磁共振颅脑图像分割中的应用研究

目的　介绍一种动态模糊聚类算法并利用该算法对磁共振图像进行分割研究。方法　首先对磁共振颅脑图像进行预处理去掉颅骨和肌肉等非脑组织,只保留大脑组织,然后利用模糊Ｋ－ 均值聚类算法计算脑白质、脑灰质和脑脊液的模糊类属函数。结果　模糊Ｋ－ 均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、白质和脑脊液。结论　利用模糊Ｋ－ 均值聚类算法分割磁共振颅脑图像能获得较好的分割效果。     

Simple paradigm for extra-cerebral tissue removal: algorithm and analysis

Extraction of the brain-i.e. cerebrum, cerebellum, and brain stem-from T1-weighted structural magnetic resonance images is an important initial step in neuroimage analysis. Although automatic algorithms are available, their inconsistent handling of the cortical mantle often requires manual interaction, thereby reducing their effectiveness. This paper presents a fully automated brain extraction algorithm that incorporates elastic registration, tissue segmentation, and morphological techniques which are combined by a watershed principle, while paying special attention to the preservation of the boundary between the gray matter and the cerebrospinal fluid. The approach was evaluated by comparison to a manual rater, and compared to several other leading algorithms on a publically available data set of brain images using the Dice coefficient and containment index as performance metrics. The qualitative and quantitative impact of this initial step on subsequent cortical surface generation is also presented. Our experiments demonstrate that our approach is quantitatively better than six other leading algorithms (with statistical significance on modern T1-weighted MR data). We also validated the robustness of the algorithm on a very large data set of over one thousand subjects, and showed that it can replace an experienced manual rater as preprocessing for a cortical surface extraction algorithm with statistically insignificant differences in cortical surface position.     

A head phantom prototype to verify subdural electrode localization tools in epilepsy surgery

When planning epilepsy surgery, the position of subdural electrodes in relation to the cortex is crucial. Electrodes may dislocate after implantation. Neurosurgeons are highly interested in the accuracy of methods that visualize these electrodes. In order to determine the accuracy of an electrode visualization method, we have developed a physical head phantom and evaluated our new method of subdural electrode localization. This method projects automatically segmented electrodes of a preimplantation computed tomography (CT) data set onto the segmented brain surface of a postimplantation magnetic resonance imaging (MRI) data set within 2 to 5 min. The phantom consists of a skull, an adipose layer for skin replication, and a deformable brain. It further contains gyri and sulci structures, composed of gelatin and different additives used as phantom material for white matter, gray matter, and cerebrospinal fluid. The phantom allows a well-defined displacement of an “implanted” electrode grid perpendicular to the brain surface. By using the phantom data, we demonstrated that our electrode visualization tool did in fact function accurately. The image contrasts between different phantom materials in MRI and CT phantom data sets were similar to patient data sets. The phantom appears suitable for obtaining a more complex patient data replication, as well as for simulating different deformation scenarios.     

优化VBM算法和DARTEL算法分析阿尔茨海默病MRI

目的 探讨优化VBM算法和DARTEL算法在分析阿尔茨海默病（AD）患者MRI的差别。方法 利用两种算法对14例AD患者（AD组）和23名健康对照（NC组）的MRI进行分析,并对其全脑灰质进行基于体素的统计学比较。结果 两种算法均报告颞叶右侧、海马、海马沟、海马旁回、杏仁核、枕叶深部存在灰质萎缩,但DARTEL算法报告的簇明显多于优化VBM算法;另外,部分区域在设置P<0.005时优化VBM算法仍未见报告。结论 分析AD患者的MRI时,采用DARTEL算法比优化VBM算法更合理。     

The lateral asymmetry of the human brain studied by volumetric magnetic resonance imaging

Improvements in in vivo imaging methods have boosted research on brain asymmetry aimed at further establishing putative anatomical substrates for brain functional lateralization and particularly to explain left-hemisphere specialization for language. We analyzed volume asymmetries for major anatomical divisions of the lateral (perisylvian) brain region and their relative white matter content. A total of 100 healthy right-handed subjects were examined with 3D magnetic resonance imaging (MRI). The insular plane was used to limit the lateral brain, and the sylvian fissure and central sulcus to define frontal, parietal, temporal, and temporo-parieto-occipital regions. Results revealed a frontal region showing similar volumes in both hemispheres, a parietal region and a temporal region both larger in the left hemisphere, and a temporo-parieto-occipital region with predominantly right-sided asymmetry. Volume measurements of the parietal, temporal, and temporo-parieto-occipital regions complemented each other and accounted for 58% of planum temporale area variations. All study regions showed significant asymmetry for relative white matter content (percentage of white matter relative to region volume). White matter asymmetry, however, was particularly relevant for the frontal and temporal regions showing a highly frequent left-sided pattern (frontal region, 90%; temporal region, 91% of subjects). Leftward asymmetry in these two regions occurred in both genders, although hemisphere differences were significantly larger in men. Results from this MRI volume analysis of structural asymmetries in the lateral brain region complement data obtained by other methods and suggest a high occurrence of leftward asymmetry for relative white matter content in language-related regions.