id: 05695022
dt: a
an: 05695022
au: Bilotta, Eleonora; Cerasa, Antonio; Pantano, Pietro; Quattrone, Aldo;
    Staino, Andrea; Stramandinoli, Francesca
ti: A CNN based algorithm for the automated segmentation of multiple sclerosis
    lesions.
so: Di Chio, Cecilia (ed.) et al., Applications of evolutionary computation.
    EvoApplications 2010: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE,
    EvoNUM, and EvoSTOC, Istanbul, Turkey, April 7‒9, 2010. Proceedings,
    Part I. Berlin: Springer (ISBN 978-3-642-12238-5/pbk). Lecture Notes in
    Computer Science 6024, 211-220 (2010).
py: 2010
pu: Berlin: Springer
la: EN
cc: 
ut: cellular neural networks; genetic algorithms; automated magnetic resonance
    imaging analysis; multiple sclerosis lesion load
ci: 
li: doi:10.1007/978-3-642-12239-2_22
ab: Summary: 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 $256 \times 256$ 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.
rv: