Abstract
Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55∼90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18∼96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5∼18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness.
Highlights
Brain extraction, is an important preprocessing procedure in brain magnetic resonance (MR) image analysis
4.1 Compared Methods and Parameter Selection Seven popularly used methods were evaluated in comparison with the proposed method: 1) Brain Extraction Tool (BET) [23], 2) Two-pass BET (2pBET), 3) BET-B: BET with bias field correction and neck cleanup, 4) Brain Surface Extractor (BSE) [18], 5) Hybrid Watershed Algorithm (HWA) [25], 6) ROBEX [33], 7) AFNI [44]
We can conclude that the proposed method is significantly better than all other compared methods (BET, 2pBET, BET-B, BSE, HWA, ROBEX and AFNI)
Summary
Brain extraction ( known as skull stripping), is an important preprocessing procedure in brain magnetic resonance (MR) image analysis It aims to remove non-brain tissues, such as skull, dura and eyes, and retain the brain tissues, typically in a T1-weighted brain MRI scan. When applied to diverse large-scale datasets with varying scanning parameters, different age and diagnostic groups, many existing methods may only work well on certain datasets with certain parameter settings and a tremendous amount of human intervention is needed for parameter tuning across datasets. These ‘optimized’ parameters do not guarantee satisfactory results. An accurate and robust approach for the extraction of the brain automatically and consistently is highly desirable especially for diverse large-scale multi-site studies, such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset [6], Open Access Series of Imaging Studies (OASIS) dataset [7], and NIH Pediatric Database (NIHPD) [8], which could greatly reduce the need for intensive human intervention that is quite time-consuming and may cause bias or inconsistency
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