Abstract #4794
Automated lesion detection from multimodal brain MRI using Markov random fields and random forest
Jhimli Mitra 1 , Soumya Ghose 1 , Pierrick Bourgeat 1 , Olivier Salvado 1 , Stephen Rose 1 , Bruce Campbell 2 , Alan Connelly 3 , Susan Palmer 4 , Leeanne Carey 4 , and Jurgen Fripp 1
1
The Australian e-Health Research Centre,
CSIRO Computational Informatics, CSIRO
Preventative-Health Flagship, Herston, QLD, Australia,
2
Department
of Medicine and Neurology, Royal Melbourne Hospital,
Parkville, VIC, Australia,
3
Department
of Radiology, Royal Melbourne Hospital, Parkville, VIC,
Australia,
4
The Florey Institute of
Neuroscience and Mental Health, Parkville, VIC,
Australia
We present an automated method to delineate chronic
ischemic stroke lesions, white-matter hyperintensities
and other secondary lesions from multimodal MRI.
Accurate delineation of such lesions is crucial in
analyzing the structure-function relationships of the
brain post-stroke and critical in the management of
stroke patients. The method firstly employed a maximum
aposteriori-Markov field based segmentation of the
probable lesion areas from hyperintense regions of FLAIR
images. Then features of these lesion areas from the
multimodal MRI were used to train/test a random forest
classifier. The performance was evaluated on 36 stroke
patients (mean Dice 0.60+/-0.12, volume correlation
0.76)
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