Robust Deformable-Surface-Based Skull-Stripping for Large-Scale Studies

Wang, Yaping; Nie, Jingxin; Yap, Pew-Thian; Shi, Feng; Guo, Lei; Shen, Dinggang

doi:10.1007/978-3-642-23626-6_78

Cited by 89 publications

(71 citation statements)

References 18 publications

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“…This latter force can be seen as an encoding a priori knowledge about the smoothness of the inner surface of the skull. Wang et al [80] study a method with initial skull stripping by co-registration of an atlas, followed by a refinement phase with a surface deformation scheme that is guided by prior information. Active shape model-based automated skull stripping method from infantile brain MR images has been described in Kobashi et al [81].…”

Section: Atlas/template-based 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: Atlas/template-based Methodsmentioning

confidence: 99%

“…Wang et al [80] Co-registration of an atlas and deformation scheme that is guided by prior information T1-weighted images Need manual extraction of atlas-based prior information to guide surface evolution and refinement.…”

Section: T1-weighted Imagesmentioning

confidence: 99%

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

2015

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“…Specifically, an intensity inhomogeneity on the T1-weighted MR images was corrected using nonparametric nonuniform intensity normalization (N3) algorithm (Sled et al 1998). Then, a robust and automated skull stripping method was applied for brain extraction and cerebellum removal (Wang et al 2011). Each brain image is further segmented into three types of tissue volumes, e.g., gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) volumes.…”

Section: Methodsmentioning

confidence: 99%

Identifying Informative Imaging Biomarkers via Tree Structured Sparse Learning for AD Diagnosis

2013

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Neuroimaging provides a powerful tool to characterize neurodegenerative progression and therapeutic efficacy in Alzheimer’s disease (AD) and its prodromal stage—mild cognitive impairment (MCI). However, since the disease pathology might cause different patterns of structural degeneration, which is not pre-known, it is still a challenging problem to identify the relevant imaging markers for facilitating disease interpretation and classification. Recently, sparse learning methods have been investigated in neuroimaging studies for selecting the relevant imaging biomarkers and have achieved very promising results on disease classification. However, in the standard sparse learning method, the spatial structure is often ignored, although it is important for identifying the informative biomarkers. In this paper, a sparse learning method with tree-structured regularization is proposed to capture patterns of pathological degeneration from fine to coarse scale, for helping identify the informative imaging biomarkers to guide the disease classification and interpretation. Specifically, we first develop a new tree construction method based on the hierarchical agglomerative clustering of voxel-wise imaging features in the whole brain, by taking into account their spatial adjacency, feature similarity and discriminability. In this way, the complexity of all possible multi-scale spatial configurations of imaging features can be reduced to a single tree of nested regions. Second, we impose the tree-structured regularization on the sparse learning to capture the imaging structures, and then use them for selecting the most relevant biomarkers. Finally, we train a support vector machine (SVM) classifier with the selected features to make the classification. We have evaluated our proposed method by using the baseline MR images of 830 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which includes 198 AD patients, 167 progressive MCI (pMCI), 236 stable MCI (sMCI), and 229 normal controls (NC). Our experimental results show that our method can achieve accuracies of 90.2 %, 87.2 %, and 70.7 % for classifications of AD vs. NC, pMCI vs. NC, and pMCI vs. sMCI, respectively, demonstrating promising performance compared with other state-of-the-art methods.

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“…Specifically, an intensity inhomogeneity on the T1-weighted MR brain images was corrected using nonparametric non-uniform intensity normalization (N3) algorithm [7]. Then, a robust and automated skull stripping method [8] was applied for brain extraction and for cerebellum removal. Each brain image was further segmented into three tissue volumes: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF).…”

Section: Preprocessingmentioning

confidence: 99%

Multi-task Sparse Classifier for Diagnosis of MCI Conversion to AD with Longitudinal MR Images

Liu

Suk

Shen

2013

Machine Learning in Medical Imaging

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Abstract. Mild cognitive impairment (MCI) patients are at a high risk of turning into Alzheimer's disease (AD) within years. But it is known that not all MCI patients will progress to AD. Therefore, it is of great interest to accurately diagnose whether a MCI patient will convert to AD (namely MCI converter; MCI-C) or not (namely MCI non-converter; MCI-NC), for early diagnosis and proper treatment. In this paper, we propose a multi-task sparse representation classifier to discriminate between MCI-C and MCI-NC utilizing longitudinal neuroimaging data. Unlike the previous methods that explicitly combined the longitudinal information in a feature domain, thus requiring the same number of measurements in time, the proposed method is not limited to the availability of the data. Specifically, by means of multi-task learning, we impose a group constraint that the same training samples, ideally belonging to the same class, are used to represent the longitudinal feature vectors across time points. Then we utilize a sparse representation classifier for label decision. From a machine learning perspective, the proposed method can be considered as the combination of the generative and discriminative methods, which are known to be effective in classification enhancement. In our experiments on magnetic resonance brain images of 349 MCI subjects (164 MCI-C and 185 MCI-NC) from ADNI database, we demonstrate the validity of the proposed method, which also outperforms the competing methods.

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Robust Deformable-Surface-Based Skull-Stripping for Large-Scale Studies

Cited by 89 publications

References 18 publications

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

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

Identifying Informative Imaging Biomarkers via Tree Structured Sparse Learning for AD Diagnosis

Multi-task Sparse Classifier for Diagnosis of MCI Conversion to AD with Longitudinal MR Images

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