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 contribution

Abstract

© Springer Nature Switzerland AG 2019. 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 publicationCircular non-uniform sampling patch inputs for CNN applied to multiple sclerosis lesion segmentation
Pages673-680
Number of pages8
ISBN (Electronic)9783030134686
DOIs
Publication statusPublished - 1 Jan 2019
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2019 → …

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

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/19 → …

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  • Cite this

    Ulloa, G., Naranjo, R., Allende-Cid, H., Chabert, S., & Allende, H. (2019). Circular non-uniform sampling patch inputs for CNN applied to multiple sclerosis lesion segmentation. In Circular non-uniform sampling patch inputs for CNN applied to multiple sclerosis lesion segmentation (pp. 673-680). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11401 LNCS). https://doi.org/10.1007/978-3-030-13469-3_78