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End to End Trainable Active Contours via Differentiable Rendering

by   Shir Gur, et al.

We present an image segmentation method that iteratively evolves a polygon. At each iteration, the vertices of the polygon are displaced based on the local value of a 2D shift map that is inferred from the input image via an encoder-decoder architecture. The main training loss that is used is the difference between the polygon shape and the ground truth segmentation mask. The network employs a neural renderer to create the polygon from its vertices, making the process fully differentiable. We demonstrate that our method outperforms the state of the art segmentation networks and deep active contour solutions in a variety of benchmarks, including medical imaging and aerial images. Our code is available at


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Code Repositories


Deep snake algorithm for 2D images based on ICLR2020

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Official PyTorch implementation of "End to End Trainable Active Contours via Differentiable Rendering"

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