Towards an automatic lesion segmentation method for dual echo magnetic resonance images using an ensemble of neural networks

Abstract

There is a well recognised need for a robust, accurate and reproducible automatic method for identifying multiple sclerosis (MS) lesions on proton density (PD-weighted) and T2-weighted magnetic resonance images (MRI). Feed-forward neural networks (FFNN) are computational techniques inspired by the physiology of the brain and used in the approximation of general mappings from one finite dimensional space to another. They present a practical application of the theoretical resolution of Hilbert's 13th problem by Kolmogorov and Lorenz, and have been used with success in a variety of applications. We present a method for automatic lesion segmentation of fast spin echo (FSE) images (PD-weighted & T2-weighted) based on anensemble of feed-forward neural networks. The FFNN of the input layer of the ensemble are trained with different portions of example lesion and non-lesion data which have previously been hand-segmented by a clinician. The final output of the ensemble is determined by a gate FFNN which is trained to weigh the response of the input layer to unseen training data. The scheme was trained and tested with data extracted from brains suffering from MS. The results are presented. © Springer-Verlag Berlin Heidelberg 2003.