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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