Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach

Author:

Valverde Sergi,Cabezas Mariano,Roura Eloy,González-Villà Sandra,Pareto Deborah,Vilanova Joan C.,Ramió-Torrentà Lluís,Rovira Àlex,Oliver Arnau,Lladó Xavier

Funder

La Fundació la Marató de TV3

Retos de Investigación

Publisher

Elsevier BV

Subject

Cognitive Neuroscience,Neurology

Reference58 articles.

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3. Brosch, T., Tang, L.Y.W., Yoo, Y., Li, D.K.B., Traboulsee, A., Tam, R., 2016. Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation, 35(5): pp. 1229–1239.

4. BOOST: A supervised approach for multiple sclerosis lesion segmentation;Cabezas;J. Neurosci. Methods,2014

5. Improved automatic detection of new T2 lesions in multiple sclerosis using deformation fields;Cabezas;AJNR. Am. J. Neuroradiol.,2016

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