通过融合空间信息提升血管结构分割的拓扑准确性。
Topology-Preserving Image Segmentation with Spatial-Aware Persistent Feature Matching
- 引入空间感知的拓扑损失函数，结合图像原始空间信息辅助特征匹配。
- 在多种管状结构图像上显著提升分割拓扑正确率，优于现有方法。
- 适合医学图像中需精确保持拓扑结构的任务，如血管或神经束分割。
拓扑正确性对管状结构的分割至关重要，这类结构广泛存在于生物医学图像中。现有的拓扑分割损失函数主要基于图像的持久同调（persistent homology），通过匹配分割结果与真实标注的持久特征并最小化差异来实现。然而，此类方法因仅依赖拓扑空间信息，存在匹配不明确的问题。本文提出一种高效的空间感知拓扑损失函数（Spatial-Aware Topological Loss, SATLoss），进一步利用图像原始空间域的信息辅助持久特征匹配。在多种类型管状结构图像上的大量实验表明，所提方法显著提升了分割的拓扑准确性，优于当前最优方法。代码已公开于 https://github.com/JRC-VPLab/SATLoss。
原文摘要 · Abstract (English)
Topological correctness is critical for segmentation of tubular structures, which pervade in biomedical images. Existing topological segmentation loss functions are primarily based on the persistent homology of the image. They match the persistent features from the segmentation with the persistent features from the ground truth and minimize the difference between them. However, these methods suffer from an ambiguous matching problem since the matching only relies on the information in the topological space. In this work, we propose an effective and efficient Spatial-Aware Topological Loss Function that further leverages the information in the original spatial domain of the image to assist the matching of persistent features. Extensive experiments on images of various types of tubular structures show that the proposed method has superior performance in improving the topological accuracy of the segmentation compared with state-of-the-art methods. Code is available at https://github.com/JRC-VPLab/SATLoss.
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