Abstract #4353
Using Diffusion and Structural MRI for the Automated Segmentation of Multiple Sclerosis Lesions
Pedro A. Gmez 1,2 , Tim Sprenger 1,2 , Ana A. Lpez 1 , Jonathan I. Sperl 2 , Brice Fernandez 3 , Miguel Molina-Romero 1,2 , Xin Liu 1,2 , Vladimir Golkov 1,2 , Michael Czisch 4 , Philipp Saemann 4 , Marion I. Menzel 2 , and Bjoern H. Menze 1
1
Technical University Munich, Munich,
Germany,
2
GE
Global Research, Munich, Germany,
3
GE
Healthcare, Munich, Germany,
4
Max
Plank Institute of Psychiatry, Munich, Germany
This work proposes to use scalar features calculated
from diffusion MR data alongside structural MR
intensities in the automated segmentation of Multiple
Sclerosis (MS) lesions. We acquired and processed
multi-contrast MR data from 7 MS patients, used random
forests to segment lesions, and evaluated our method via
DICE scores, achieving scores over 0.65. Finally, we
made use of the random forest framework to assess the
discriminative power of the estimated features. We show
that diffusion features estimated from the diffusion
tensor are as discriminative as T1 and T2 intensities
for the classification task.
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