Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods

Iglesias, Juan Eugenio; Liu, Cheng-Yi; Thompson, Paul M.; Tu, Zhuowen

doi:10.1109/tmi.2011.2138152

Cited by 504 publications

(422 citation statements)

References 33 publications

Supporting

Mentioning

417

Contrasting

Unclassified

Order By: Relevance

“…In terms of Dice overlap, results obtained by BEaST are better than those reported from recent hybrid brain extraction approaches (Carass et al, 2011;Iglesias et al, 2011) and similar to those from a label fusion approach, MAPS (Leung et al, 2011). In the label fusion approach, the library is more than 10 times larger and the processing time about 40 times longer.…”

Section: Comparison To State Of the Artmentioning

confidence: 51%

“…We chose to compare with BET, as BET is publicly available, widely used, and has been shown to perform well in several recent brain extraction comparisons (Carass et al, 2011;Iglesias et al, 2011;Leung et al, 2011). The choice of VBM8 was based on its availability and the fact that it is the highest-ranking publicly available method in the archive of the online Segmentation Validation Engine for brain segmentation (Shattuck et al, 2009) (http://sve.loni.ucla.edu/archive/).…”

Section: Comparison To Other Methodsmentioning

confidence: 99%

“…We used BET version 2.1 from FSL version 4.1. Since BET performs better with robust brain center estimation and when the neck is not visible in the image (Iglesias et al, 2011), we applied BET on the normalized and stereotaxically aligned images with default parameters.…”

Section: Comparison To Other Methodsmentioning

confidence: 99%

“…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%

See 3 more Smart Citations

BEaST: Brain extraction based on nonlocal segmentation technique

et al. 2012

View full text Add to dashboard Cite

-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...

show abstract

Section: Comparison To State Of the Artmentioning

confidence: 51%

Section: Comparison To Other Methodsmentioning

confidence: 99%

Section: Comparison To Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

BEaST: Brain extraction based on nonlocal segmentation technique

et al. 2012

View full text Add to dashboard Cite

show abstract

“…ROBEX [96] is a RObust, learning-based Brain EXtraction system. This method combines the discriminative and generative model to achieve the final results.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

View full text Add to dashboard Cite

The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.

show abstract

Multimodal structural neuroimaging markers of risk and recovery from posttrauma anhedonia: A prospective investigation

Harnett

Stevens

Rooij

et al. 2020

Depression and Anxiety

View full text Add to dashboard Cite

Background: Anhedonic symptoms of posttraumatic stress disorder (PTSD) reflect deficits in reward processing that have significant functional consequences. Although recent evidence suggests that disrupted integrity of fronto-limbic circuitry is related to PTSD development, including anhedonic PTSD symptoms (posttrauma anhedonia [PTA]), little is known about potential structural biomarkers of long-term PTA as well as structural changes in fronto-limbic pathways associated with recovery from PTA over time. Methods: We investigated associations between white matter microstructure, gray matter volume, and PTA in 75 recently traumatized individuals, with a subset of participants (n = 35) completing follow-up assessment 12 months after trauma exposure. Deterministic tractography and voxel-based morphometry were used to assess changes in white and gray matter structure associated with changes in PTA. Results: Reduced fractional anisotropy (FA) of the uncinate fasciculus at around the time of trauma predicted greater PTA at 12-months posttrauma. Further, increased FA of the fornix over time was associated with lower PTA between 1 and 12-months posttrauma. Increased gray matter volume of the ventromedial prefrontal cortex and precuneus over time was also associated with reduced PTA. Conclusions: The microstructure of the uncinate fasciculus, an amygdala-prefrontal white matter connection, may represent a biomarker of vulnerability for later PTA. Conversely, development and recovery from PTA appear to be facilitated by white and gray matter structural changes in a major hippocampal pathway, the fornix. The present findings shed new light on neuroanatomical substrates of recovery from PTA and characterize white matter biomarkers of risk for posttraumatic dysfunction.

show abstract

Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods

Cited by 504 publications

References 33 publications

BEaST: Brain extraction based on nonlocal segmentation technique

BEaST: Brain extraction based on nonlocal segmentation technique

Methods on Skull Stripping of MRI Head Scan Images—a Review

Multimodal structural neuroimaging markers of risk and recovery from posttrauma anhedonia: A prospective investigation

Contact Info

Product

Resources

About