Nonlocal Patch-Based Label Fusion for Hippocampus Segmentation

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

doi:10.1007/978-3-642-15711-0_17

Cited by 47 publications

(52 citation statements)

References 18 publications

(46 reference statements)

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“…In Coupé et al (2010Coupé et al ( , 2011b, we showed that accurate segmentations of anatomical structures can be obtained using this simple patch-based label fusion framework. In addition, to take advantage of the self-similarity of the image as done for denoising, the nonlocal label fusion also relies on intersubject anatomical consistency.…”

Section: Methods Overviewmentioning

confidence: 99%

“…In Coupé et al (2010Coupé et al ( , 2011b, we were the first to introduce the nonlocal means estimator in the context of segmentation by averaging labels instead of intensities. By using a training library of N subjects, whose segmentations of structures are known, the weighted label fusion is estimated as follows: (3) where l(x s,j ) is the label (i.e., 0 for background and 1 for structure) given by the expert to the voxel x s,j at location j in training subject s. It has been shown that the nonlocal means estimator v(x i ) provides a robust estimation of the expected label at x i .…”

Section: Methods Overviewmentioning

confidence: 99%

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Simultaneous segmentation and grading of anatomical structures for patient's classification: Application to Alzheimer's disease

et al. 2012

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Section: Methods Overviewmentioning

confidence: 99%

Section: Methods Overviewmentioning

confidence: 99%

Simultaneous segmentation and grading of anatomical structures for patient's classification: Application to Alzheimer's disease

et al. 2012

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“…The proposed Brain Extraction based on nonlocal Segmentation Technique (BEaST), is inspired by the patch-based segmentation first published in Coupé et al (2010) and extended in . As done in Coupé et al (2011), we use sum of squared differences (SSD) as the metric for estimation of distance between patches.…”

Section: Proposed Brain Extraction 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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“…A typical patch-based segmentation searches each region of the image to find the most similar patches within an atlas, a training database with known ground truth segmentation. The atlas is hence aligned with the image and a final segmentation is obtained by fusing the votes from selected patches of atlas images (Coupè et al, 2010;Rousseau et al, 2011).…”

Section: Brain Extractionmentioning

confidence: 99%

Automated fetal brain segmentation from 2D MRI slices for motion correction

et al. 2014

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Motion correction is a key element for imaging the fetal brain in-utero using Magnetic Resonance Imaging (MRI). Maternal breathing can introduce motion, but a larger effect is frequently due to fetal movement within the womb. Consequently, imaging is frequently performed slice-by-slice using single shot techniques, which are then combined into volumetric images using slice-tovolume reconstruction methods (SVR). For successful SVR, a key preprocessing step is to isolate fetal brain tissues from maternal anatomy before correcting for the motion of the fetal head. This has hitherto been a manual or semi-automatic procedure. We propose an automatic method to localize and segment the brain of the fetus when the image data is acquired as stacks of 2D slices with anatomy misaligned due to fetal motion. We combine this segmentation process with a robust motion correction method, enabling the segmentation to be refined as the reconstruction proceeds. The fetal brain localization process uses Maximally Stable Extremal Regions (MSER), which are classified using a Bag-of-Words model with Scale-Invariant Feature Transform (SIFT) features. The segmentation process is a patch-based propagation of the MSER regions selected during detection, combined with a Conditional Random Field (CRF). The gestational age (GA) is used to incorporate prior knowledge about the size and volume of the fetal brain into the detection and segmentation process. The method was tested in a ten-fold cross-validation experiment on 66 datasets of healthy fetuses whose GA ranged from 22 to 39 weeks. In 85% of the tested cases, our proposed method produced a motion corrected volume of a relevant quality for clinical diagnosis, thus removing the need for manually delineating the contours of the brain before motion correction. Our method automatically generated as a side-product a segmentation of the reconstructed fetal brain with a mean Dice score of 93%, which can be used for further processing.

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Nonlocal Patch-Based Label Fusion for Hippocampus Segmentation

Cited by 47 publications

References 18 publications

Simultaneous segmentation and grading of anatomical structures for patient's classification: Application to Alzheimer's disease

Simultaneous segmentation and grading of anatomical structures for patient's classification: Application to Alzheimer's disease

BEaST: Brain extraction based on nonlocal segmentation technique

Automated fetal brain segmentation from 2D MRI slices for motion correction

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