3D UNet with GAN discriminator for robust localisation of the fetal brain and trunk in MRI with partial coverage of the fetal body
bioRxiv, 2021•biorxiv.org
In fetal MRI, automated localisation of the fetal brain or trunk is a prerequisite for motion
correction methods. However, the existing CNN-based solutions are prone to errors and
may require manual editing. In this work, we propose to combine a multi-label 3D UNet with
a GAN discriminator for localisation of both fetal brain and trunk in fetal MRI stacks. The
proposed method is robust for datasets with both full and partial coverage of the fetal body.
correction methods. However, the existing CNN-based solutions are prone to errors and
may require manual editing. In this work, we propose to combine a multi-label 3D UNet with
a GAN discriminator for localisation of both fetal brain and trunk in fetal MRI stacks. The
proposed method is robust for datasets with both full and partial coverage of the fetal body.
Abstract
In fetal MRI, automated localisation of the fetal brain or trunk is a prerequisite for motion correction methods. However, the existing CNN-based solutions are prone to errors and may require manual editing. In this work, we propose to combine a multi-label 3D UNet with a GAN discriminator for localisation of both fetal brain and trunk in fetal MRI stacks. The proposed method is robust for datasets with both full and partial coverage of the fetal body.
biorxiv.org
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