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 Poppel^1,2, Johannes K. Steinweg¹, Irina Grigorescu¹, Alexia Egloff Collado³, Paula Ramirez Gilliland¹, Thomas A. Roberts¹, Joseph V. Hajnal^1,3, Mary Rutherford³, David F.A. Lloyd^1,2, Kuberan Pushparajah^1,2, 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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