NABS: non-local automatic brain hemisphere segmentation

Abstract

In this paper, we propose an automatic method to segment the five main brain sub-regions (i.e. left/right hemispheres, left/right cerebellum and brainstem) from magnetic resonance images.The proposed method uses a library of pre-labeled brain images in a stereotactic space in combination with a non local label fusion scheme for segmentation. The main novelty of the proposed method is the use of a multi-label block-wise label fusion strategy specifically designed to deal with the classification of main brain sub-volumes that process only specific parts of the brain images significantly reducing the computational burden. The proposed method has been quantitatively evaluated against manual segmentations. The evaluation showed that the proposed method was faster while producing more accurate segmentations than a current state-of-the-art method. We also present evidences suggesting that the proposed method was more robust against brain pathologies than the compared method. Finally, we demonstrate the clinical value of our method compared to the state-of-the-art approach in terms of the asymmetry quantification in Alzheimer's disease.

Review of Computational Methods on Brain Symmetric and Asymmetric Analysis from Neuroimaging Techniques

“…Romero et al [54] presented an accurate and fast patch based multi template brain segmentation method, termed the NABS (Non-Local Automatic Brain Hemisphere Segmentation), for segmenting cerebral and cerebellar hemispheres and the brainstem from T1-weighted MR brain images. This NABS method was used to accurately delineate brain structures in healthy subjects across a wide range of ages.…”

“…Techniques Used Image Modality [44] Surface projection PET [45] Linear Snake Modal, Orthogonal regression MRI, CT [46] Intensity gradient based method MRI [47] Shape bottleneck algorithm MRI [48] Non rigid registration and template matching MRI [49] Central sulcus measuring 3D MRI [50] Fractal dimension MRI [18] Extended shape bottleneck algorithm and partial volume estimate 3D MRI [51] Content based cellular neural networks method and image registration 3D, 2D MRI [52] Adaptive disconnection method 3D MRI [53] Non localized label fusion and template MRI [54] NABS method and patch based multi template segmentation T1 Weighted MRI Most of these existing methods are applicable to MRI of the brain and are also sensitive to image noises, imaging artifacts such as aliasing and orientation deviations [55]. Therefore, there is a greater need to develop an automatic, efficient and robust computational method to quantify the symmetry/asymmetry of human brain images from different imaging modalities for various biomedical and neuroscientific applications.…”

Review of Computational Methods on Brain Symmetric and Asymmetric Analysis from Neuroimaging Techniques

“…Image modality [36] Surface projection PET [37] Linear Snake Modal, Orthogonal regression MRI, CT [38] Intensity gradient based method MRI [39] Shape bottleneck algorithm MRI [40] Non rigid registration and template matching MRI [41] Central sulcus measuring 3D MRI [42] Fractal dimension MRI [43] Extended shape bottleneck algorithm and partial volume estimate 3D MRI [44] Content based cellular neural networks method and image registration 3D, 2D MRI [45] Adaptive disconnection method 3D MRI [46] Non localized label fusion and template MRI [47] NABS method and patch based multi template segmentation T1 Weighted MRI number of training subjects. The result comparisons carried out between appearances based method and the template based method in MR brain images.…”

“…The result comparisons carried out between appearances based method and the template based method in MR brain images. Romero et al [47] presented an accurate and fast patch based multi template brain segmentation method, termed NABS (Non-Local Automatic Brain Hemisphere Segmentation), for segmenting cerebral and cerebellar hemispheres and brainstem from T1-weighted MR brain images. This NABS method was used to accurately delineate brain structures in healthy subjects across a wide range of ages.…”

LesionBrain: An Online Tool for White Matter Lesion Segmentation

“…Structure segmentation: The T1w is used to segment several anatomical structures. First, the intracranial cavity (ICC) is extracted using [21] and brainstem and cerebellum using [22]. Finally, lateral ventricles are segmented using [23].…”