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Abstract #4818

Brain tissue segmentation in fetal MRI using convolutional neural networks with simulated intensity inhomogeneities

Nadieh Khalili1, Nikolas Lessmann1, Elise Turk2,3, Max Viergever1,3, Manon Benders2,3, and Ivana Isgum1,3

1Image Sciences Institute, Utrecht University, Utrecht, The Netherlands, Utrecht, Netherlands, 2Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, The Netherlands, Utrecht, Netherlands, 3Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands, Utrecht, Netherlands

Automatic brain tissue segmentation in fetal MRI is a challenging task due to artifacts such as intensity inhomogeneity, caused in particular by spontaneous fetal movements during the scan. Unlike methods that estimate the bias field to remove intensity inhomogeneity as a preprocessing step in segmentation process, we aim at improving the robustness of the segmentation method by introducing an intensity inhomogeneity augmentation (IIA). The IIA simulates various patterns of intensity inhomogeneity during the training of the segmentation network. The segmentation results demonstrate an improvement in segmentation performance when the training data is augmented with IIA.

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