Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study.

ABSTRACT:

Motivation

Ischemic stroke, triggered by an obstruction in the cerebral blood supply, leads to infarction of the affected brain tissue. An accurate and reproducible automatic segmentation is of high interest, since the lesion volume is an important end-point for clinical trials. However, various factors, such as the high variance in lesion shape, location and appearance, render it a difficult task.

Methods

In this article, nine classification methods (e.g. Generalized Linear Models, Random Decision Forests and Convolutional Neural Networks) are evaluated and compared with each other using 37 multiparametric MRI datasets of ischemic stroke patients in the sub-acute phase in terms of their accuracy and reliability for ischemic stroke lesion segmentation. Within this context, a

SUBMITTER: Maier O 

PROVIDER: S-EPMC4687679 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature