新损失函数提升图像分割拓扑正确性，修复44%更多断连
ContextLoss: Context Information for Topology-Preserving Segmentation
- 基于结构上下文设计新损失函数，聚焦拓扑错误区域
- 在3个数据集上修复最多44%的断连，显著提升连通性
- 适合需要精确结构拓扑的医学与工业图像分割任务
在图像分割中，保持血管、膜或道路等结构的拓扑完整性至关重要。近期方法通过关键像素掩码中的整体骨架定义损失函数。本文提出新型损失函数ContextLoss（CLoss），通过考虑关键像素掩码中拓扑错误的完整上下文来提升拓扑正确性。额外上下文使网络更关注拓扑错误。我们还提出两个直观度量以验证连接性改善。在三个公开数据集（2D与3D）及自建的骨水泥线3D纳米成像数据集上进行基准测试。使用CLoss训练后，拓扑感知指标性能提升，相比其他先进方法修复最多44%的断连。代码已开源。
原文摘要 · Abstract (English)
In image segmentation, preserving the topology of segmented structures like vessels, membranes, or roads is crucial. For instance, topological errors on road networks can significantly impact navigation. Recently proposed solutions are loss functions based on critical pixel masks that consider the whole skeleton of the segmented structures in the critical pixel mask. We propose the novel loss function ContextLoss (CLoss) that improves topological correctness by considering topological errors with their whole context in the critical pixel mask. The additional context improves the network focus on the topological errors. Further, we propose two intuitive metrics to verify improved connectivity due to a closing of missed connections. We benchmark our proposed CLoss on three public datasets (2D & 3D) and our own 3D nano-imaging dataset of bone cement lines. Training with our proposed CLoss increases performance on topology-aware metrics and repairs up to 44% more missed connections than other state-of-the-art methods. We make the code publicly available.
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