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Abstract #3255

Dual Fully Convolutional Networks for Multiscale Context based Robust MRI Skull Stripping

Pascal Ceccaldi1, Benjamin Odry1, Boris Mailhe1, and Mariappan Nadar1

1Medical Imaging Technologies, Siemens Healthineers, Princeton, NJ, United States

Brain Segmentation is a standard preprocessing step for neuroimaging applications, but can however be subject to differences in MR acquisition that can lead to added noise, bias field and / or partial volume effect. To address those protocol differences, we therefore present a generic supervised framework, using consecutively two deep learning networks, to produce a fast and accurate brain extraction aimed at being robust across MR protocol variations. While we only trained our network on Human Connectome Project 3T dataset, we can still achieve state-of-the-art results on1.5T cases from LPBA dataset.

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