Circular Non-uniform Sampling Patch Inputs for CNN Applied to Multiple Sclerosis Lesion Segmentation
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.
KeywordsConvolutional Neural Networks Image segmentation Multiple sclerosis lesions Magnetic resonance imaging Non-uniform patch
This work was supported by the Fondecyt Grant 1170123 and in part by Fondecyt Grant FB0821. Héctor Allende-Cid is supported by project Fondecyt Initiation into Research 11150248. Steren Chabert is supported by Centro de Investigación y Desarrollo en Ingeniería en Salud (CINGS).
The research of Gustavo Ulloa is also supported by the Incentive program for scientific initiation (PIIC) of the Universidad Técnica Federico Santa María DGIIP.
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