Abstract #0270
3D MRI atlases of congenital aortic arch anomalies and normal fetal heart: application to automated multi-label segmentation
Alena Uus1, Milou P.M. van Poppel1,2, Johannes K. Steinweg1, Irina Grigorescu1, Alexia Egloff Collado3, Paula Ramirez Gilliland1, Thomas A. Roberts1, Joseph V. Hajnal1,3, Mary Rutherford3, David F.A. Lloyd1,2, Kuberan Pushparajah1,2, and Maria Deprez1
1Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Department of Congenital Heart Disease, Evelina London Children’s Hospital, London, United Kingdom, 3Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
Synopsis
This work introduces the first black blood 3D T2w MRI atlases of the normal fetal heart and congenital aortic arch anomalies. The atlases were generated from 87 subjects from normal, CoA, RAA and DAA cohorts and also include multi-label segmentations of the major cardiovascular structures. We further evaluated the feasibility of using deep learning for automated multi-label vessel segmentation in 3D T2w motion-corrected MRI images of the fetal heart.
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