@inproceedings{2f7be2024060445eb36799aa82dc798d,
title = "Circular non-uniform sampling patch inputs for CNN applied to multiple sclerosis lesion segmentation",
abstract = "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.",
keywords = "Convolutional Neural Networks, Image segmentation, Magnetic resonance imaging, Multiple sclerosis lesions, Non-uniform patch",
author = "Gustavo Ulloa and Rodrigo Naranjo and H{\'e}ctor Allende-Cid and Steren Chabert and H{\'e}ctor Allende",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018 ; Conference date: 19-11-2018 Through 22-11-2018",
year = "2019",
doi = "10.1007/978-3-030-13469-3_78",
language = "English",
isbn = "9783030134686",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "673--680",
editor = "Ruben Vera-Rodriguez and Julian Fierrez and Aythami Morales",
booktitle = "Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 23rd Iberoamerican Congress, CIARP 2018, Proceedings",
}