Abstract #1748
Average probabilistic brain atlases for post-mortem newborn and fetal populations and application to tissue segmentation
Eliza Orasanu 1 , Andrew Melbourne 1 , M. Jorge Cardoso 1 , Marc Modat 1 , Andrew M. Taylor 2 , Sudhin Thayyil 3 , and Sebastien Ourselin 1
1
Centre of Medical Image Computing,
University College London, London, United Kingdom,
2
Centre
for Cardiovascular Imaging, Institute of Cardiovascular
Science, University College London, London, United
Kingdom,
3
University
College Hospital, London, United Kingdom
Segmentation of the fetal and neonatal brain magnetic
resonance (MR) imaging is useful for understanding both
normal and abnormal brain development, however it is
challenging due to post-mortem artefacts and changes in
T1 and T2 tissue values after death. In this paper we
create average probabilistic brain atlases for newborn
and fetus populations and use them for the automatic
segmentation of further subjects with similar morphology
from the same study. We compare them with manual
segmentations, obtaining good agreement. This paper is
the first to successfully generate post-mortem brain
atlases from MR images of neonates and fetuses fully
automatically.
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