Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains

Rohlfing, Torsten; Brandt, Robert; Menzel, Randolf; Maurer, Calvin R.

doi:10.1016/j.neuroimage.2003.11.010

Cited by 517 publications

(463 citation statements)

References 27 publications

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“…Moreover, the number of labeled samples defining the segmentation (which depends on the ratio between the volume of the structure and the voxel size) (Rohlfing et al, 2004) can impact the similarity measure. Nonetheless, interesting tendencies in method evolution can be extracted by studying published results.…”

Section: Discussionmentioning

confidence: 99%

“…Second, template-warping methods usually use a majority voting scheme to fuse the labels (Aljabar et al, 2009;Collins and Pruessner, 2010;Heckemann et al, 2006;Rohlfing et al, 2004) that considers the relevance (or weight) of all the samples labeled as similar. In the proposed method, the intensity-based distances between the patch under study and the patches in the training subjects are used to perform a weighted label fusion based on the nonlocal means estimator (Buades et al, 2005).…”

Section: Methods Overviewmentioning

confidence: 99%

“…In contrast to brain tissue classification where the intensity of the MR signal can be used to segment different tissue types, anatomical segmentation usually requires information derived from the manual segmentations done by experts (i.e., expert priors), since anatomical structures can be composed of several tissue types and distinct anatomical structures can have the same MR signal properties. To overcome this difficulty, several automatic methods of segmentation have been proposed, such as deformable models or region growing (Chupin et al, 2007;Ghanei et al, 1998;Shen et al, 2002), appearance-based models (Duchesne et al, 2002;Hu and Collins, 2007), and atlas/template-warping techniques (Aljabar et al, 2009;Barnes et al, 2008;Collins et al, 1995;Fischl et al, 2002;Gousias et al, 2008;Hammers et al, 2007;Heckemann et al, 2006;Rohlfing et al, 2004;Zhou and Rajapakse, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation

et al. 2011

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Section: Discussionmentioning

confidence: 99%

Section: Methods Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation

et al. 2011

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“…The difference in DSCs between the techniques may seem small. However, when measuring DSC in the context of whole brain segmentations, small changes in the coefficient correspond to large changes in volume as demonstrated in (Rohlfing et al, 2004 depending on brain size and the false positives -false negatives ratio. This volume is relatively large when compared to the size of the structures, which are usually measured in neuroimaging studies (e.g.…”

Section: Comparison To Other Methodsmentioning

confidence: 99%

“…; the size of the human hippocampus is about 3.5 cm 3 ). The varying bias of the DSC when segmenting structures of different sizes (Rohlfing et al, 2004) in our case is considered low, as the brains have been spatially normalized. The FPRs and FNRs shown in Fig.…”

Section: Comparison To Other Methodsmentioning

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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Muscle strength, functional performance, and self‐reported outcomes four years after arthroscopic partial meniscectomy in middle‐aged patients

Ericsson¹,

Roos²,

Dahlberg³

2006

Arthritis & Rheumatism

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Objective. To examine thigh muscle strength, functional performance, and self-reported outcome in patients with nontraumatic meniscus tears 4 years after operation, and to study the impact of a strength deficit on self-reported outcome and evaluate the feasibility of 3 performance tests in this patient group. Methods. The study group comprised 45 patients (36% women, mean age 46.7) who had an arthroscopic partial meniscectomy a mean of 4 years (range 1-6 years) previously. Main outcome measures included isokinetic strength of knee extensors and flexors, functional performance (1-leg hop, 1-leg rising, and square-hop tests), and a self-reported questionnaire (Knee Injury and Osteoarthritis Outcome Score). Results. We found lower knee extensor strength and worse 1-leg rising capacity in the operated leg, but no difference between operated and nonoperated leg for knee flexors (P < 0.004 and P > 0.3, respectively). Patients with a stronger quadriceps of the operated leg compared with the nonoperated leg had less pain and better function and quality of life (r ‫؍‬ 0.4 -0.6, P < 0.010). We found the 1-leg rising and 1-leg hop tests to be suitable performance tests in middle-aged meniscectomy patients. Conclusion. Quadriceps strength is reduced in the meniscectomized leg compared with the nonoperated leg 4 years after surgery. This relative quadriceps weakness significantly affects objective and self-reported knee function, pain, and quality of life, indicating the importance of restoring muscle function after meniscectomy in middle-aged patients.

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Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains

Cited by 517 publications

References 27 publications

Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation

Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation

BEaST: Brain extraction based on nonlocal segmentation technique

Muscle strength, functional performance, and self‐reported outcomes four years after arthroscopic partial meniscectomy in middle‐aged patients

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