Image Segmentation with Homotopy Warping
- URL: http://arxiv.org/abs/2112.07812v1
- Date: Wed, 15 Dec 2021 00:33:15 GMT
- Title: Image Segmentation with Homotopy Warping
- Authors: Xiaoling Hu, Chao Chen
- Abstract summary: topological correctness is crucial for the segmentation of images with fine-scale structures.
By leveraging the theory of digital topology, we identify locations in an image that are critical for topology.
We propose a new homotopy warping loss to train deep image segmentation networks for better topological accuracy.
- Score: 10.093435601073484
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Besides per-pixel accuracy, topological correctness is also crucial for the
segmentation of images with fine-scale structures, e.g., satellite images and
biomedical images. In this paper, by leveraging the theory of digital topology,
we identify locations in an image that are critical for topology. By focusing
on these critical locations, we propose a new homotopy warping loss to train
deep image segmentation networks for better topological accuracy. To
efficiently identity these topologically critical locations, we propose a new
algorithm exploiting the distance transform. The proposed algorithm, as well as
the loss function, naturally generalize to different topological structures in
both 2D and 3D settings. The proposed loss function helps deep nets achieve
better performance in terms of topology-aware metrics, outperforming
state-of-the-art topology-preserving segmentation methods.
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