Circular non-uniform sampling patch inputs for CNN applied to multiple sclerosis lesion segmentation

Gustavo Ulloa, Rodrigo Naranjo, Héctor Allende-Cid, Steren Chabert, Héctor Allende

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

Convolutional Neural Networks (CNN) have been obtaining successful results in the task of image segmentation in recent years. These methods use as input the sampling obtained using square uniform patches centered on each voxel of the image, which could not be the optimal approach since there is a very limited use of global context. In this work we present a new construction method for the patches by means of a circular non-uniform sampling of the neighborhood of the voxels. This allows a greater global context with a radial extension with respect to the central voxel. This approach was applied on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, obtaining better results than approaches using square uniform and non-uniform patches with the same computational cost of the CNN models.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 23rd Iberoamerican Congress, CIARP 2018, Proceedings
EditoresRuben Vera-Rodriguez, Julian Fierrez, Aythami Morales
EditorialSpringer Verlag
Páginas673-680
Número de páginas8
ISBN (versión impresa)9783030134686
DOI
EstadoPublicada - 2019
Publicado de forma externa
Evento23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018 - Madrid, Espana
Duración: 19 nov. 201822 nov. 2018

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11401 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018
País/TerritorioEspana
CiudadMadrid
Período19/11/1822/11/18

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