Resumen de Data preprocessing for automatic WMH segmentation with FCNNs

P. Duque, José Manuel Cuadra Troncoso, E. Jiménez, Mariano Rincón Zamorano

• Automatic segmentation of brain white matter hyperintensities(WMH) is a challenging problem. Recently, the proposals basedon Fully Convolutional Neural Networks (FCNN) are giving very good results, as it is demostrated by the top WMH challenge architectures.However, the problem is non completely solved yet. In this paper we analyze the influence of preprocessing stages of the input data on a fully convolutional network (FCNN) based on the U-NET architecture. Results demostrate that standarization, skull stripping and contrast enhancement significantly influence the results of segmentation.