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

Carass, Aaron; Cuzzocreo, Jennifer L.; Wheeler, Matt; Bazin, Pierre‐Louis; Resnick, Susan M.; Prince, Jerry L.

doi:10.1016/j.neuroimage.2011.03.045

Cited by 113 publications

(112 citation statements)

References 36 publications

(65 reference statements)

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“…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%

“…Following this principle, we accept inclusion of internal CSF and CSF proximate to the brain, as the T1w MR signal from CSF is easily separated from non-liquid structures and subsequent analyses may benefit from the inclusion of CSF as noted in (Carass et al, 2011). We propose the following definition of a mask separating the brain from non-brain tissue: Excluded from the mask  Skull, skin, muscles, fat, eyes, dura mater, bone and bone marrow  Exterior blood vessels -specifically the carotid arteries, the superior sagittal sinus and the transverse sinus  Exterior nerves -specifically the optic chiasms…”

Section: Definition Of Brain Maskmentioning

confidence: 99%

“…While there is consensus among methods that obvious non-brain structures, such as skull, dura, and eyes should be removed as part of the brain extraction process, there are divergent opinions on other structures and tissues, such as the amount of extra-cerebral cerebro-spinal fluid (CSF), blood vessels, and nerves. Some methods define the target segmentation as white matter (WM) and gray matter (GM) only (Leung et al, 2011), while others include CSF, veins, and the optic chiasms (Carass et al, 2011;Smith, 2002). Depending on the objective for the subsequent analysis it is important to remove tissues that may be confused with brain tissue in the images.…”

Section: Introductionmentioning

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).…”

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confidence: 99%

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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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Section: Comparison To State Of the Artmentioning

confidence: 51%