End to End Trainable Active Contours via Differentiable Rendering
This method addresses segmentation problems in domains such as medical imaging and aerial analysis, offering a novel integration of active contours with deep learning.
The paper tackles image segmentation by evolving a polygon using a differentiable renderer and encoder-decoder architecture, achieving state-of-the-art performance on benchmarks like medical and aerial imaging.
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 https://github.com/shirgur/ACDRNet.