Quantitative comparison of four brain extraction algorithms

Boesen, Kristi; Rehm, Kelly; Schaper, Kirt; Stoltzner, Sarah E.; Woods, Roger P.; Lüders, Eileen; Rottenberg, David A.

doi:10.1016/j.neuroimage.2004.03.010

Cited by 112 publications

(77 citation statements)

References 9 publications

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“…Several comparative studies [14,[97][98][99][100] have been carried out on some of the existing skull stripping methods. Lee et al [97] compared the performance of the two automated methods (BET [31] and BSE [36]) and two semi-automated methods (ANALYZE 4.0 [101] and modified region growing (mRG) proposed by Yoon et al [102]).…”

Section: Comparative Studies On Skull Stripping Methodsmentioning

confidence: 99%

“…A study by Boesen et al [98] compared the McStrip [89] method with SPM2 [53], BET [31], and BSE [36] using T1-weighted MR brain volumes. McStrip is a hybrid algorithm based on intensity thresholding, nonlinear warping, and edge detection.…”

Section: Comparative Studies On Skull Stripping Methodsmentioning

confidence: 99%

“…Their results showed that with proper parameter selection, all the three algorithms can achieve satisfactory skull stripping on the test dataset. A comparative study on various methods [14,[97][98][99][100] revealed that HWA [91] has the highest sensitivity in general but the lowest specificity. HWA [91] seems to be more robust to the change of parameters than other methods.…”

Section: Comparative Studies On Skull Stripping Methodsmentioning

confidence: 99%

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

2015

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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.

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Section: Comparative Studies On Skull Stripping Methodsmentioning

confidence: 99%

Section: Comparative Studies On Skull Stripping Methodsmentioning

confidence: 99%

Section: Comparative Studies On Skull Stripping Methodsmentioning

confidence: 99%

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

2015

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“…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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“…The main steps of FEMTool were derived from an existing method named brain extraction tool (BET) [10]- [13] and re-designed to accommodate the characteristics of our femoral QCT dataset.…”

Section: Femtool For Femur Segmentationmentioning

confidence: 99%

An Automated Method to Estimate Femoral Shape and Mineral Mass

Pau¹,

Masala²,

Bao³

et al. 2012

International Journal of Computational Bioscience

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Medical assessment of bone health often uses quantitative computed tomography (QCT) scans and requires a reliable segmentation of bone geometry from surrounding tissues for a quick determination of bone mineral mass. Because of its shape and position in the body, the femur is one of the most challenging bones to investigate. In the current study, we developed a new automated way to accurately evaluate both the shape and the mineral mass of cadaveric femur.The results were achieved through a series of steps including the segmentation of bone tissue from sets of QCT images, the estimation of the bone's outer surface, the calculation of the volume enclosed, and finally the evaluation of bone mineral mass in a user-defined region. We compared our algorithms outputs to results obtained by expert manual segmentation and those obtained using other published methods.

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Quantitative comparison of four brain extraction algorithms

Cited by 112 publications

References 9 publications

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

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

BEaST: Brain extraction based on nonlocal segmentation technique

An Automated Method to Estimate Femoral Shape and Mineral Mass

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