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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


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.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 23rd Iberoamerican Congress, CIARP 2018, Proceedings
EditorsRuben Vera-Rodriguez, Julian Fierrez, Aythami Morales
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783030134686
StatePublished - 2019
Event23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018 - Madrid, Spain
Duration: 19 Nov 201822 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11401 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018


  • Convolutional Neural Networks
  • Image segmentation
  • Magnetic resonance imaging
  • Multiple sclerosis lesions
  • Non-uniform patch


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