Dataset Information

Optimized brain extraction for pathological brains (optiBET).

ABSTRACT: The study of structural and functional magnetic resonance imaging data has greatly benefitted from the development of sophisticated and efficient algorithms aimed at automating and optimizing the analysis of brain data. We address, in the context of the segmentation of brain from non-brain tissue (i.e., brain extraction, also known as skull-stripping), the tension between the increased theoretical and clinical interest in patient data, and the difficulty of conventional algorithms to function optimally in the presence of gross brain pathology. Indeed, because of the reliance of many algorithms on priors derived from healthy volunteers, images with gross pathology can severely affect their ability to correctly trace the boundaries between brain and non-brain tissue, potentially biasing subsequent analysis. We describe and make available an optimized brain extraction script for the pathological brain (optiBET) robust to the presence of pathology. Rather than attempting to trace the boundary between tissues, optiBET performs brain extraction by (i) calculating an initial approximate brain extraction; (ii) employing linear and non-linear registration to project the approximate extraction into the MNI template space; (iii) back-projecting a standard brain-only mask from template space to the subject's original space; and (iv) employing the back-projected brain-only mask to mask-out non-brain tissue. The script results in up to 94% improvement of the quality of extractions over those obtained with conventional software across a large set of severely pathological brains. Since optiBET makes use of freely available algorithms included in FSL, it should be readily employable by anyone having access to such tools.

SUBMITTER: Lutkenhoff ES

PROVIDER: S-EPMC4267825 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Optimized brain extraction for pathological brains (optiBET).

Lutkenhoff Evan S ES Rosenberg Matthew M Chiang Jeffrey J Zhang Kunyu K Pickard John D JD Owen Adrian M AM Monti Martin M MM

PloS one 20141216 12

The study of structural and functional magnetic resonance imaging data has greatly benefitted from the development of sophisticated and efficient algorithms aimed at automating and optimizing the analysis of brain data. We address, in the context of the segmentation of brain from non-brain tissue (i.e., brain extraction, also known as skull-stripping), the tension between the increased theoretical and clinical interest in patient data, and the difficulty of conventional algorithms to function op ...[more]

PMID: 25514672

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Project description:Brain extraction from 3D medical images is a common pre-processing step. A variety of approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images exhibiting strong pathologies, for example, the presence of a brain tumor or of a traumatic brain injury (TBI), is challenging. In such cases, tissue appearance may substantially deviate from normal tissue appearance and hence violates algorithmic assumptions for standard approaches to brain extraction; consequently, the brain may not be correctly extracted. This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue appearance and pathologies. Specifically, our model uses a three-part image decomposition: (1) normal tissue appearance is captured by principal component analysis (PCA), (2) pathologies are captured via a total variation term, and (3) the skull and surrounding tissue is captured by a sparsity term. Due to its convexity, the resulting decomposition model allows for efficient optimization. Decomposition and image registration steps are alternated to allow statistical modeling of normal tissue appearance in a fixed atlas coordinate system. As a beneficial side effect, the decomposition model allows for the identification of potentially pathological areas and the reconstruction of a quasi-normal image in atlas space. We demonstrate the effectiveness of our approach on four datasets: the publicly available IBSR and LPBA40 datasets which show normal image appearance, the BRATS dataset containing images with brain tumors, and a dataset containing clinical TBI images. We compare the performance with other popular brain extraction models: ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing approaches on all four datasets. Specifically, our model achieves the best median (97.11) and mean (96.88) Dice scores over all datasets. The two best performing competitors, ROBEX and MASS, achieve scores of 96.23/95.62 and 96.67/94.25 respectively. Hence, our approach is an effective method for high quality brain extraction for a wide variety of images.

Project description:IntroductionA hallmark of Alzheimer's disease (AD) is the presence of senile plaques composed of aggregated amyloid β (Aβ) peptides. Pathological aging (PA) is a postmortem classification that has been used to describe brains with plaque pathology similar in extent to AD, minimal cortical tau pathology, and no accompanying history of cognitive decline in the brain donor prior to death. PA may represent either a prodromal phase of AD, a benign form of Aβ accumulation, or inherent individual resistance to the toxic effects of Aβ accumulation. To attempt to distinguish between these possibilities we have systematically characterized Aβ peptides in a postmortem series of PA, AD and non-demented control (NDC) brains.MethodsAβ was sequentially extracted with tris buffered saline (TBS), radioimmunoprecipitation buffer (RIPA), 2% sodium dodecyl sulfate (SDS) and 70% formic acid (FA) from the pre-frontal cortex of 16 AD, eight PA, and six NDC patients. These extracts were analyzed by 1) a panel of Aβ sandwich ELISAs, 2) immunoprecipitation followed by mass spectrometry (IP/MS) and 3) western blotting. These studies enabled us to asses Aβ levels and solubility, peptide profiles and oligomeric assemblies.ResultsIn almost all extracts (TBS, RIPA, 2% SDS and 70% FA) the average levels of Aβ1-40, Aβ1-42, Aβ total, and Aβx-42 were greatest in AD. On average, levels were slightly lower in PA, and there was extensive overlap between Aβ levels in individual PA and AD cases. The profiles of Aβ peptides detected using IP/MS techniques also showed extensive similarity between the PA and AD brain extracts. In select AD brain extracts, we detected more amino-terminally truncated Aβ peptides compared to PA patients, but these peptides represented a minor portion of the Aβ observed. No consistent differences in the Aβ assemblies were observed by western blotting in the PA and AD groups.ConclusionsWe found extensive overlap with only subtle quantitative differences between Aβ levels, peptide profiles, solubility, and SDS-stable oligomeric assemblies in the PA and AD brains. These cross-sectional data indicate that Aβ accumulation in PA and AD is remarkably similar. Such data would be consistent with PA representing a prodromal stage of AD or a resistance to the toxic effects of Aβ.

Dataset Information

Optimized brain extraction for pathological brains (optiBET).

Publications

Optimized brain extraction for pathological brains (optiBET).

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