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Abstract #3428

Introducing MANTis: Morphological adaptive neonate tissue segmentation. Unified segmentation for neonates

Richard Beare 1 , Jian Chen 1 , Dimitrios Alexopoulos 2 , Christopher Smyser 2 , Cynthia Rogers 2 , Wai Yen Loh 1,3 , Lillian Gabra Fam 1 , Claire Kelly 1 , Jeanie Cheong 1,4 , Alicia Spittle 1 , Peter Anderson 1,5 , Lex Doyle 1,4 , Terrie Inder 6 , Jeff Neil 6 , Marc Seal 1 , and Deanne Thompson 1

1 Murdoch Childrens Research Institute, Parkville, Victoria, Australia, 2 Washington University in St Louis, MO, United States, 3 Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia, 4 Royal Women's Hospital, Parkville, Victoria, Australia, 5 Paediatrics, University of Melbourne, Parkville, Victoria, Australia, 6 Brigham and Women's Hospital, Massachusettes, United States

Tissue classification in MR scans of neonates, especially preterm neonates, is challenging and has lead to a number of different automated approaches, with varying degrees of success. One issue in previous methods has been the degree of adaptability required due to the range of pathologies observed in studies of premature infants. This project addresses the issue of providing sufficient adaptability while maintaining stability and eliminating user intervention by combining morphological methods with the well-established unified segmentation approach from SPM.

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