Automatic brain tissue segmentation in fetal MRI using convolutional neural networks

• Published in: Magnetic resonance imaging Vol. 64; pp. 77 - 89
• Main Authors: Khalili, N., Lessmann, N., Turk, E., Claessens, N., Heus, R. de, Kolk, T., Viergever, M.A., Benders, M.J.N.L., Išgum, I.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.12.2019
• Subjects: Brain - abnormalities; Brain - diagnostic imaging; Brain - embryology; Brain Diseases - diagnostic imaging; Brain Diseases - embryology; Brain segmentation; Convolutional neural network; Deep learning; Female; Fetal MRI; Humans; Image Interpretation, Computer-Assisted - methods; Intensity inhomogeneity; Magnetic Resonance Imaging - methods; Neural Networks, Computer; Pregnancy; Prenatal Diagnosis - methods; Deep learning; Brain segmentation; Intensity inhomogeneity; Fetal MRI; Convolutional neural network

MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segmentation is cumbersome and time consuming, hence auto...