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End-To-End Learned Random Walker for Seeded Image Segmentation
AbstractWe 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 un- derlying graph using a convolutional neural network. This can be interpreted as learning context-dependent diffusiv- ities for a linear diffusion process. After calculating the exact gradient for optimizing these diffusivities, we pro- pose simplifications that sparsely sample the gradient while still maintaining competitive results. The proposed method achieves the currently best results on the seeded CREMI neuron segmentation challenge.
Related Material
[pdf] [supp][bibtex]@InProceedings{Cerrone_2019_CVPR,
author = {Cerrone, Lorenzo and Zeilmann, Alexander and Hamprecht, Fred A.},
title = {End-To-End Learned Random Walker for Seeded Image Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
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