Whole mouse brain segmentation is an essential prerequisite for multiple quantitative image analysis tasks and pipelines. In this work, we compare three methods for whole brain segmentation (skull stripping) relying on active contours, graph cuts and convolutional neural networks. We applied these methods on mouse brain manganese enhanced MR images acquired at 100 micrometre isotropic resolution, in vivo. All three methods achieved Dice coefficients larger than 94%, but convolutional neural networks achieved a small but significant improvement (0.97±0.01) over our active contours implementation (0.94±0.05) and the difference approached significance relative to graph cuts (0.96±0.01).
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