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Abstract #4779

Automating fetal brain reconstruction using distance regression learning

Lucilio Cordero-Grande1, Anthony N Price1, Emer J Hughes2, Robert Wright3, Mary A Rutherford2, and Joseph V Hajnal1

1Centre for the Developing Brain and Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 3Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

We describe a method for automated fetal brain reconstruction from stacks of 2D single-shot slices. Brain localization is performed by a deep distance regression network. Slice alignment is accomplished by a global search in the rigid transform space followed by registration using a fractional derivative metric. An outlier robust hybrid $$$1,2$$$-norm and linear high order regularization are used for reconstruction. Brain localization has achieved competitive results without requiring annotated segmentations. The method has produced acceptable reconstructions in 129 out of 133 3T fetal examinations tested so far.

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