A CNN based algorithm for the automated segmentation of multiple sclerosis lesions

Authors:
Eleonora Bilotta;Antonio Cerasa;Pietro Pantano;Aldo Quattrone;Andrea Staino;Francesca Stramandinoli
Affiliations:
Evolutionary Systems Group, University of Calabria, Cosenza, Italy;Neuroimaging Research Unit, Institute of Neurological Sciences, National Research Council, Catanzaro, Italy;Evolutionary Systems Group, University of Calabria, Cosenza, Italy;Neuroimaging Research Unit, Institute of Neurological Sciences, National Research Council, Catanzaro, Italy;Evolutionary Systems Group, University of Calabria, Cosenza, Italy;Evolutionary Systems Group, University of Calabria, Cosenza, Italy
Venue:
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Year:
2010

Citing 2
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Cellular Neural Networks and Visual Computing

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Multiple Network CGP for the Classification of Mammograms

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Abstract

We describe a new application based on genetic algorithms (GAs) that evolves a Cellular Neural Network (CNN) capable to automatically determine the lesion load in multiple sclerosis (MS) patients from Magnetic Resonance Images (MRI). In particular, it seeks to identify in MRI brain areas affected by lesions, whose presence is revealed by areas of lighter color than the healthy brain tissue. In the first step of the experiment, the CNN has been evolved to achieve better performances for the analysis of MRI. Then, the algorithm was run on a data set of 11 patients; for each one 24 slices, each with a resolution of 256 ×256 pixels, were acquired. The results show that the application is efficient in detecting MS lesions. Furthermore, the increased accuracy of the system, in comparison with other approaches, already implemented in the literature, greatly improves the diagnosis for this disease.