Abstract:Identifying fetal orientation is essential for determining the mode of delivery and for sequence planning in fetal magnetic resonance imaging (MRI). This manuscript describes a deep learning algorithm named Fet-Net, composed of convolutional neural networks (CNNs), which allows for the automatic detection of fetal orientation from a two-dimensional (2D) MRI slice. The architecture consists of four convolutional layers, which feed into a simple artificial neural network. Compared with eleven other prominent CNN… Show more
“…Eisenstat et al addressed the task of automated fetal MRI planning [ 78 ]. Determining the fetus’s presentation is an important element in the sequence planning, as it affects the mode of delivery.…”
Over the last decade, artificial intelligence (AI) has made an enormous impact on a wide range of fields, including science, engineering, informatics, finance, and transportation [...]
“…Eisenstat et al addressed the task of automated fetal MRI planning [ 78 ]. Determining the fetus’s presentation is an important element in the sequence planning, as it affects the mode of delivery.…”
Over the last decade, artificial intelligence (AI) has made an enormous impact on a wide range of fields, including science, engineering, informatics, finance, and transportation [...]
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