Abstract #4305
AUTOMATED BRAIN EXTRACTION IN FETAL MRI BY MULTI-ATLAS FUSION STRATEGY: STUDY ON HEALTHY AND PATHOLOGICAL SUBJECTS.
Sbastien Tourbier 1,2 , Xavier Bresson 1,2 , Patric Hagmann 2 , Maud Cagneaux 3 , Marie Schaer 4 , Laurent Guibaud 3 , Jean-Philippe Thiran 2,5 , Reto Meuli 2 , and Meritxell Bach Cuadra 1,2
1
Centre d'Imagerie BioMdicale (CIBM),
Lausanne, Vaud, Switzerland,
2
Department
of Radiology, University Hospital Center (CHUV) and
University of Lausanne, Lausanne, Vaud, Switzerland,
3
Department
of Radiology, Hpital Femme-Mre-Enfant (HFME), Lyon,
Rhne, France,
4
Department
of Psychiatry, School of Medicine, University of Geneva,
Geneva, Switzerland,
5
Signal
Processing Laboratory (LTS5), Ecole Polytechnique
Fdrale de Lausanne (EPFL), Lausanne, Vaud, Switzerland
In fetal brain MRI, most of the high-resolution
reconstruction algorithms rely on brain segmentation as
a preprocessing step. Manual brain segmentation is
highly time-consuming and therefore not a realistic
solution for large-scale studies. Only few works have
addressed this automatic extraction problem. In this
study, we assess the validity of Multiple Atlas Fusion
(MAF) strategies to automatically segment the fetal
brain in MR imaging. We show that MAF performance is
increased in healthy brain by increasing the number of
atlases. Secondly, we also show that MAF can be applied
to pathological brains even when large anatomical
differences are present.
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