Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods

Ahmed Serag, Manuel Blesa, Emma J. Moore, Rozalia Pataky, Sarah A. Sparrow, A. G. Wilkinson, Gillian Macnaught, Scott I. Semple, James P. Boardman
2016 Scientific Reports  
Accurate whole-brain segmentation, or brain extraction, of magnetic resonance imaging (MRI) is a critical first step in most neuroimage analysis pipelines. The majority of brain extraction algorithms have been developed and evaluated for adult data and their validity for neonatal brain extraction, which presents age-specific challenges for this task, has not been established. We developed a novel method for brain extraction of multi-modal neonatal brain MR images, named ALFA (Accurate Learning
more » ... ith Few Atlases). The method uses a new sparsity-based atlas selection strategy that requires a very limited number of atlases 'uniformly' distributed in the low-dimensional data space, combined with a machine learning based label fusion technique. The performance of the method for brain extraction from multimodal data of 50 newborns is evaluated and compared with results obtained using eleven publicly available brain extraction methods. ALFA outperformed the eleven compared methods providing robust and accurate brain extraction results across different modalities. As ALFA can learn from partially labelled datasets, it can be used to segment large-scale datasets efficiently. ALFA could also be applied to other imaging modalities and other stages across the life course. Magnetic resonance imaging (MRI) is a powerful technique for assessing the brain because it can provide cross-sectional and longitudinal high-resolution images with good soft tissue contrast. It is well-suited to studying brain development in early life, investigating environmental and genetic influences on brain growth during a critical period of development, and to extract biomarkers of long term outcome and neuroprotective treatment effects in the context of high risk events such as preterm birth and birth asphyxia 1-7 . Whole-brain segmentation, also known as brain extraction or skull stripping, is the process of segmenting an MR image into brain and non-brain tissues. It is the first step in most neuroimage pipelines including: brain tissue segmentation and volumetric measurement 8-12 ; template construction 13-15 ; longitudinal analysis [16] [17] [18] [19] ; and cortical and sub-cortical surface analysis 20-23 . Accurate brain extraction is critical because under-or over-estimation of brain tissue voxels cannot be salvaged in successive processing steps, which may lead to propagation of error through subsequent analyses. Several brain extraction methods have been developed and evaluated for adult data. These can be classified into non-learning-and learning-based approaches. Non-learning-based approaches assume a clear separation between brain and non-brain tissues, and no training data are required. For instance, the Brain Extraction Tool (BET) uses a deformable surface model to detect the brain boundaries based on local voxel intensity and surface smoothness 24 , while the Brain Surface Extractor (BSE) methodology combines morphological operation with edge detection 25 . 3dSkullStrip (3DSS) from the AFNI toolkit 26 is a modified version of BET in order to avoid segmentation of eyes and ventricles and reduce leakage into the skull. The Hybrid Watershed Algorithm 27 combines
doi:10.1038/srep23470 pmid:27010238 pmcid:PMC4806304 fatcat:mzjusbtyarbtvjjmuavnnst2gy