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• W4380091421 endingPage "102608" @default.
• W4380091421 startingPage "102608" @default.
• W4380091421 abstract "Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal imaging. Different aspects challenge the segmentation of the fetal brain in magnetic resonance imaging (MRI), such as the non-standard position of the fetus owing to his/her movements during the examination, rapid brain development, and the limited availability of imaging data. In recent years, several segmentation methods have been proposed for automatically partitioning the fetal brain from MR images. These algorithms aim to define regions of interest with different shapes and intensities, encompassing the entire brain, or isolating specific structures. Deep learning techniques, particularly convolutional neural networks (CNNs), have become a state-of-the-art approach in the field because they can provide reliable segmentation results over heterogeneous datasets. Here, we review the deep learning algorithms developed in the field of fetal brain segmentation and categorize them according to their target structures. Finally, we discuss the perceived research gaps in the literature of the fetal domain, suggesting possible future research directions that could impact the management of fetal MR images." @default.
• W4380091421 created "2023-06-10" @default.
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• W4380091421 date "2023-09-01" @default.
• W4380091421 modified "2023-10-18" @default.
• W4380091421 title "Review on deep learning fetal brain segmentation from Magnetic Resonance images" @default.
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• W4380091421 doi "https://doi.org/10.1016/j.artmed.2023.102608" @default.
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• W4380091421 hasPublicationYear "2023" @default.
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