Data preprocessing for automatic WMH segmentation with FCNNs

• Autores: P. Duque, José Manuel Cuadra Troncoso, E. Jiménez, Mariano Rincón Zamorano
• Localización: From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II / coord. por Hojjat Adeli; José Manuel Ferrández Vicente (dir. congr.), José Ramón Álvarez Sánchez (dir. congr.), Félix de la Paz López (dir. congr.), Francisco Javier Toledo Moreo (dir. congr.), 2019, ISBN 978-3-030-19651-6, págs. 452-460
• Idioma: inglés
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• Resumen

  • 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.