Abstract:We use deep max-pooling convolutional neural networks to address a problem of
neuroanatomy, namely, the automatic segmentation of cerebral cortex structures of laboratory rat depicted in stacks of Two-photon microscopy images and detect the change areas when stimulation occurs. We classify each pixel in the image by training a CNN network, using a square window to predict the probability of the central pixel for each class. After classification, we perform the post-processing on the output produced by CNN. At … Show more
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