Structure-Aware Image Segmentation with Homotopy Warping
Structure-Aware Image Segmentation with Homotopy Warping
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发表时间：
2021-12
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通讯作者：
Xiaoling Hu
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作者：
Xiaoling Hu
除了每像素的准确性之外，拓扑正确性对于具有精细尺度结构的图像的分割也是至关重要的，例如，卫星图像和生物医学图像。在本文中，通过利用数字拓扑理论，我们确定在图像中的像素是拓扑结构的关键。通过关注这些关键像素，我们提出了一种新的同伦扭曲损失来训练深度图像分割网络，以获得更好的拓扑准确性。为了有效地识别这些拓扑关键像素，我们提出了一种新的算法，利用距离变换。所提出的算法，以及损失函数，自然推广到不同的拓扑结构，在2D和3D设置。提出的损失函数有助于深度网络在拓扑感知度量方面实现更好的性能，优于最先进的结构/拓扑感知分割方法。
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 pixels in an image that are critical for topology. By focusing on these critical pixels, we propose a new homotopy warping loss to train deep image segmentation networks for better topological accuracy. To efficiently identify these topologically critical pixels, 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 structure/topology-aware segmentation methods.