Abstract
Fetal brain magnetic resonance imaging (MRI) is a rapidly emerging diagnostic imaging tool. However, automated fetal brain localization is one of the biggest obstacles in expediting and fully automating large-scale fetal MRI processing. We propose a method for automatic localization of fetal brain in 3 T MRI when the images are acquired as a stack of 2D slices that are misaligned due to fetal motion. First, the Histogram of Oriented Gradients (HOG) feature descriptor is extended from 2D to 3D images. Then, a sliding window is used to assign a score to all possible windows in an image, depending on the likelihood of it containing a brain, and the window with the highest score is selected. In our evaluation experiments using a leave-one-out cross-validation strategy, we achieved 96% of complete brain localization using a database of 104 MRI scans at gestational ages between 34 and 38 weeks. We carried out comparisons against template matching and random forest based regression methods and the proposed method showed superior performance. We also showed the application of the proposed method in the optimization of fetal motion correction and how it is essential for the reconstruction process. The method is robust and does not rely on any prior knowledge of fetal brain development.
Highlights
Recent successful application of magnetic resonance imaging (MRI) has provided us with an unprecedented opportunity to study, in intricate detail, the developing brain in the living fetus or neonate [1,2,3,4,5,6,7]
Such reconstruction methods rely on initial localization and cropping of the brain region from a standard wide field of view (FOV) MRI, to assist the sliceto-volume registration process [17] by excluding surrounding maternal tissues that can result in registration failure
Gradient strengths vary over a wide range due to intensity inhomogeneity; effective local contrast normalization between overlapping blocks is essential for good performance
Summary
Recent successful application of magnetic resonance imaging (MRI) has provided us with an unprecedented opportunity to study, in intricate detail, the developing brain in the living fetus or neonate [1,2,3,4,5,6,7]. Advances in medical image processing techniques have facilitated the reconstruction of motion-corrected highresolution 3D fetal MR images [8,9,10,11,12] from stacks of 2D intersecting images, which in turn have laid the foundation for modeling [13,14,15] and quantitative analysis [1, 15, 16] of the developing fetal brain Such reconstruction methods rely on initial localization and cropping of the brain region from a standard wide field of view (FOV) MRI, to assist the sliceto-volume registration process [17] by excluding surrounding maternal tissues that can result in registration failure. Taimouri et al [19] proposed another template matching approach
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