Neural ODEs for Image Segmentation with Level SetsDownload PDF

25 Sept 2019 (modified: 05 May 2023)ICLR 2020 Conference Blind SubmissionReaders: Everyone
Keywords: neural odes, level sets, image segmentation
TL;DR: We propose a novel approach for image segmentation that combines Neural Ordinary Differential Equations (NODEs) and the Level Set method.
Abstract: 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
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