Convolutional neural networks (CNN) have been widely used for visual recognition tasks including semantic segmentation of images. While the existing methods consider uniformly sampled single- or multi-scale patches from the neighborhood of each voxel, this approach might be sub-optimal as it captures and processes unnecessary details far away from the center of the patch. We instead propose to train CNNs with non-uniformly sampled patches that allow a wider extent for the sampled patches. This results in more captured contextual information, which is in particular of interest for biomedical image analysis, where the anatomical location of imaging features are often crucial. We evaluate and compare this strategy for white matter hyperintensity segmentation on a test set of 46 MRI scans. We show that the proposed method not only outperforms identical CNNs with uniform patches of the same size (0.780 Dice coefficient compared to 0.736), but also gets very close to the performance of an independent human expert (0.796 Dice coefficient)
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