An Image-Processing System for Qualitative and Quantitative Volumetric Analysis of Brain Images

Goldszal, Alberto; Davatzikos, Christos; Pham, Dzung L.; Yan, Michelle; Bryan, R. Nick; Resnick, Susan M.

doi:10.1097/00004728-199809000-00030

Cited by 273 publications

(229 citation statements)

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“…At each center, scans were performed according to standardized protocols following the supervision of the MRI Reading Center (University of Pennsylvania) (Launer et al., 2015). Image processing, quality control checks, and automated brain tissue volume computations were performed at the MRI reading center, following previously described protocols (Goldszal et al., 1998; Lao et al., 2008; Launer et al., 2015; Shen & Davatzikos, 2002; Zacharaki, Kanterakis, Bryan, & Davatzikos, 2008). In brief, all imaging data were first converted to Neuroimaging Informatics Technology Initiative (NIFTI) format.…”

Section: Methodsmentioning

confidence: 99%

Sex hormones and brain volumes in a longitudinal study of middle‐aged men in the CARDIA study

et al. 2017

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IntroductionSeveral findings suggest that testosterone (T) is neuroprotective and that declining T levels during aging are associated with cognitive and brain pathologies; however, little is known on T and brain health in middle‐age. We examined the relationships of total T, bioavailable T, and sex hormone binding globulin (SHBG) levels with total and regional gray matter (GM) and white matter (WM) volumes in middle‐aged men. We also evaluated the association of sex hormone levels with cognitive function.MethodsAnalysis included 267 community‐dwelling men participating in the Coronary Artery Risk Development in Young Adults (CARDIA) brain magnetic resonance imaging (MRI) substudy. Total T, bioavailable T, and SHBG levels were measured at three times from the 2nd to 4th decade of life; brain volumes were measured at the ages of 42–56. Associations were estimated using linear regression models, adjusted for several potential confounders.ResultsHigher SHBG levels were associated with greater total WM volume (+3.15 cm3 [95% confidence interval [CI] = 0.01, 6.28] per one standard deviation higher SHBG). Higher SHBG levels were associated with lower total and regional GM volumes overall and significantly with smaller parietal GM volume (−0.96 cm3 [95%CI = −1.71, −0.21]). T levels were not related to brain volumes. Neither T nor SHBG levels were associated with cognitive function.ConclusionResults suggest a role for SHBG in structural brain outcomes in men and emphasize the value of investigating SHBG levels as modulators of sex hormone and metabolic pathways regulating brain and behavioral characteristics in men.

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Section: Methodsmentioning

confidence: 99%

Sex hormones and brain volumes in a longitudinal study of middle‐aged men in the CARDIA study

et al. 2017

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“…As the first step of the process (see Fig. 2), an input brain image is preprocessed to remove skin, bone, fat, and other noncerebral tissues using a semiautomated software package developed by Christos Davatzikos and Jerry Miller at the Johns Hopkins University (Goldszal et al, 1998). Fig.…”

Section: Preprocessingmentioning

confidence: 99%

CRUISE: Cortical reconstruction using implicit surface evolution

et al. 2004

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Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images play an important role in neuroscience and medicine. A successful segmentation method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. A method for the automatic reconstruction of the inner, central, and outer surfaces of the cerebral cortex from T1-weighted MR brain images is presented. The method combines a fuzzy tissue classification method, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model (TGDM). The algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from selfintersections. Validation results on real MR data are presented to demonstrate the performance of the method. D 2004 Elsevier Inc. All rights reserved.

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“…The number of methods proposed to address the brain segmentation problem reflects the importance of accurate and robust brain extraction. During the last 15 years, more than 20 brain extraction methods have been proposed using a variety of techniques, such as morphological operations (Goldszal et al, 1998;Lemieux et al, 1999;Mikheev et al, 2008;Park and Lee, 2009;Sandor and Leahy, 1997;Ward, 1999), atlas matching (Ashburner and Friston, 2000;Kapur et al, 1996), deformable surfaces (Dale et al, 1999;Smith, 2002), level sets (Baillard et al, 2001;Zhuang et al, 2006), histogram analysis (Shan et al, 2002), watershed (Hahn andPeitgen, 2000), graph cuts (Sadananthan et al, 2010), label fusion (Leung et al, 2011), and hybrid techniques (Carass et al, 2011;Iglesias et al, 2011;Rehm et al, 2004;Rex et al, 2004;Segonne et al, 2004;Shattuck et al, 2001). Studies evaluating these methods have found varying accuracy (Boesen et al, 2004;Fennema-Notestine et al, 2006;Hartley et al, 2006;Lee et al, 2003;Park and Lee, 2009;Shattuck et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

BEaST: Brain extraction based on nonlocal segmentation technique

et al. 2012

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-Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases.In testing, a mean Dice similarity coefficient of 0.9834±0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781±0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors.Keywords: Brain extraction, skull stripping, patch-based segmentation, multi-resolution, MRI, BET ** Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. IntroductionBrain extraction (or skull stripping) is an important step in many neuroimaging analyses, such as registration, tissue classification, and segmentation. While methods such as the estimation of intensity normalization fields and registration do not require perfect brain masks, other methods such as measuring cortical thickness rely on very accurate brain extraction to work properly. For instance, failure to remove the dura may lead to an overestimation of cortical thickness (van der Kouwe et al., 2008), while removing part of the brain would lead to an underestimation. In cases of incorrect brain extraction, subjects may be excluded from further processing, a potentially expensive consequence for many studies. The solution of manually correcting the brain masks is a labour intensive and time-consuming task that is highly sensitive to inter-and intra-rater variability (Warfield et al., 2004).An accurate brain extraction method should exclude all tissues external to the brain, such as skull, dura, and eyes, without removing any part of the brain. The number of methods proposed to address the brain segmentation problem reflects the importance of accurate and robust brain extraction. During th...

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An Image-Processing System for Qualitative and Quantitative Volumetric Analysis of Brain Images

Cited by 273 publications

References 32 publications

Sex hormones and brain volumes in a longitudinal study of middle‐aged men in the CARDIA study

Sex hormones and brain volumes in a longitudinal study of middle‐aged men in the CARDIA study

CRUISE: Cortical reconstruction using implicit surface evolution

BEaST: Brain extraction based on nonlocal segmentation technique

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