1 research outputs found
Neural ODEs for Image Segmentation with Level Sets
We propose a novel approach for image segmentation that combines Neural
Ordinary Differential Equations (NODEs) and the Level Set method. Our approach
parametrizes the evolution of an initial contour with a NODE that implicitly
learns from data a speed function describing the evolution. In addition, for
cases where an initial contour is not available and to alleviate the need for
careful choice or design of contour embedding functions, we propose a
NODE-based method that evolves an image embedding into a dense per-pixel
semantic label space. We evaluate our methods on kidney segmentation (KiTS19)
and on salient object detection (PASCAL-S, ECSSD and HKU-IS). In addition to
improving initial contours provided by deep learning models while using a
fraction of their number of parameters, our approach achieves F scores that are
higher than several state-of-the-art deep learning algorithms