Abstract #4715
Fast and fully automatic differentiation of patients with idiopathic Parkinsonian syndrome and progressive supranuclear palsy using T1-weighted MRI datasets
Nils Daniel Forkert 1 , Jan Sedlacik 2 , and Kai Boelmans 3
1
Department of Radiology, Stanford
University, Stanford, CA, United States,
2
Department
of Diagnostic and Interventional Neuroradiology,
University Medical Center Hamburg-Eppendorf, Germany,
3
Department
of Psychiatry and Psychotherapy, University Medical
Center Hamburg-Eppendorf, Germany
The differentiation of the progressive nuclear palsy (PSP)
from the idiopathic Parkinsonian syndrome (IPS) based on
clinical criteria is often difficult and high failure
rates have been reported. This work presents a fully
automatic method for the automatic differentiation of
these two neurological diseases using an atlas-based
analysis of high-resolution T1-weighted datasets for
regional brain volume determination and subsequent
classification using a support vector machine. A first
evaluation based on 78 datasets revealed that the
proposed method is capable of differentiating IPS (n=57)
and PSP patients (n=21) fully automatically in less than
10 minutes and an accuracy of 87.2%.
This abstract and the presentation materials are available to members only;
a login is required.
Join Here
