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

Coupé, Pierrick; Manjón, José V.; Fonov, Vladimir S.; Pruessner, Jens C.; Robles, Montserrat; Collins, D. Louis

doi:10.1016/j.neuroimage.2010.09.018

Cited by 641 publications

(914 citation statements)

References 51 publications

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“…As done in Coupé et al (2011), we use sum of squared differences (SSD) as the metric for estimation of distance between patches. Using SSD as the similarity metric requires that the intensity of brain tissue is consistent across subjects and imaging sequences.…”

Section: Proposed Brain Extraction Methodsmentioning

confidence: 99%

“…The proposed method is an extension of the patch-based segmentation method described in Coupé et al (2011). In brief, a label is applied to a given voxel in the target image based on the similarity of its surrounding patch P(x i ) to all the patches P(x s,j ) in the library within a search volume.…”

Section: Patch-based Segmentationmentioning

confidence: 99%

“…The parameter h of the weighting function is locally adapted as in Coupé et al (2011) by using the minimal distance found between the patch under study and the patches of the library.…”

Section: Patch-based Segmentationmentioning

confidence: 99%

“…Patch pre-selection: Finally, to reduce the number of patches to consider, preselection of the most similar patches is done as proposed in Coupé et al (2011) using the patch mean and variance. The main idea is that similar patches should have similar means and similar variances.…”

Section: Patch-based Segmentationmentioning

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: Proposed Brain Extraction Methodsmentioning

confidence: 99%

Section: Patch-based Segmentationmentioning

confidence: 99%

“…The parameter h of the weighting function is locally adapted as in Coupé et al (2011) by using the minimal distance found between the patch under study and the patches of the library.…”

Section: Patch-based Segmentationmentioning

confidence: 99%

Section: Patch-based Segmentationmentioning

confidence: 99%

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BEaST: Brain extraction based on nonlocal segmentation technique

et al. 2012

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“…Intensity‐weighted multi‐atlas label fusion method 18 , 19 , 20 was used for ROI autosegmentation (bone, prostate, rectum, and bladder) on the subject‐MR image. Auto‐segmentation of soft‐tissue ROIs (bladder, rectum and prostate) were checked visually and manually corrected if autosegmentation was unacceptable.…”

Section: Methodsmentioning

confidence: 99%

MR image‐based synthetic CT for IMRT prostate treatment planning and CBCT image‐guided localization

Chen

Hong

Qin

et al. 2016

J Applied Clin Med Phys

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The purpose of this study was to propose and evaluate a method of creating a synthetic CT (S‐CT) from MRI simulation for dose calculation and daily CBCT localization. A pair of MR and CT images was obtained in the same day from each of 10 prostate patients. The pair of MR and CT images was preregistered using the deformable image registration (DIR). Using the corresponding displacement vector field (atlas‐DVF), the CT image was deformed to the MR image to create an atlas MR‐CT pair. Regions of interest (ROI) on the atlas MR‐CT pair were delineated and used to create atlas‐ROI masks. ‘Leave‐one‐out’ test (one pair of MR and CT was used as subject‐MR and subject‐CT for evaluation, and the remaining 9 pairs were in the atlas library) was performed. For a subject‐MR, autosegmentation and DVFs were generated using DIR between the subject‐MR and the 9 atlas‐MRs. An S‐CT was then generated using the corresponding 9 paired atlas‐CTs, the 9 atlas‐DVFs and the corresponding atlas‐ROI masks. The total 10 S‐CTs were evaluated using the Hounsfield unit (HU), the calculated dose distribution, and the auto bony registration to daily CBCT images with respect to the 10 subject‐CTs. HU differences false(mean±STDfalse) were false(2.4±25.23false), false(1.18±39.49false), false(32.46±81.9false), false(0.23±40.13false), and false(3.74±144.76false) for prostate, bladder, rectal wall, soft tissue outside all ROIs, and bone, respectively. The discrepancy of dose‐volume parameters calculated using the S‐CT for treatment planning was small (≤0.22% with 95% confidence). Gamma pass rate (2% & 2 mm) was higher than 99.86% inside PTV and 98.45% inside normal structures. Using the 10 S‐CTs as the reference CT for daily CBCT localization achieved the similar results compared to using the subject‐CT. The translational vector differences were within 1.08 mm false(0.37±0.23 mmfalse), and the rotational differences were within 1.1° in all three directions. S‐CT created from a simulation MR image using the proposed approach with the preconstructed atlas library can replace the planning CT for dose calculation and daily CBCT image guidance.PACS number(s): 87.57.nf, 87.57.nm

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Use of Flutemetamol F 18–Labeled Positron Emission Tomography and Other Biomarkers to Assess Risk of Clinical Progression in Patients With Amnestic Mild Cognitive Impairment

Wolk

Sadowsky

Safirstein³

et al. 2018

JAMA Neurol

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IMPORTANCE Patients with amnestic mild cognitive impairment (aMCI) may progress to clinical Alzheimer disease (AD), remain stable, or revert to normal. Earlier progression to AD among patients who were β-amyloid positive vs those who were β-amyloid negative has been previously observed. Current research now accepts that a combination of biomarkers could provide greater refinement in the assessment of risk for clinical progression. OBJECTIVE To evaluate the ability of flutemetamol F 18 and other biomarkers to assess the risk of progression from aMCI to probable AD. DESIGN, SETTING, AND PARTICIPANTS In this multicenter cohort study, from November 11, 2009, to January 16, 2014, patients with aMCI underwent positron emission tomography (PET) at baseline followed by local clinical assessments every 6 months for up to 3 years. Patients with aMCI (365 screened; 232 were eligible) were recruited from 28 clinical centers in Europe and the United States. Physicians remained strictly blinded to the results of PET, and the standard of truth was an independent clinical adjudication committee that confirmed or refuted local assessments. Flutemetamol F 18-labeled PET scans were read centrally as either negative or positive by 5 blinded readers with no knowledge of clinical status.

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

Cited by 641 publications

References 51 publications

BEaST: Brain extraction based on nonlocal segmentation technique

BEaST: Brain extraction based on nonlocal segmentation technique

MR image‐based synthetic CT for IMRT prostate treatment planning and CBCT image‐guided localization

Use of Flutemetamol F 18–Labeled Positron Emission Tomography and Other Biomarkers to Assess Risk of Clinical Progression in Patients With Amnestic Mild Cognitive Impairment

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