  We present an end-to-end learned algorithm for seeded segmentation. Our
method is based on the Random Walker algorithm, where we predict the edge
weights of the underlying graph using a convolutional neural network. This can
be interpreted as learning context-dependent diffusivities for a linear
diffusion process. Besides calculating the exact gradient for optimizing these
diffusivities, we also propose simplifications that sparsely sample the
gradient and still yield competitive results. The proposed method achieves the
currently best results on a seeded version of the CREMI neuron segmentation
challenge.
