提出新损失函数增强曲线结构分割的连通性，解决传统方法易出拓扑错误的问题。
CAPE: Connectivity-Aware Path Enforcement Loss for Curvilinear Structure Delineation
- 基于图结构和最短路径算法，动态选择节点对并优化连接路径
- 在神经元与血管数据集上，拓扑准确率显著提升，优于现有最优方法
- 适合需要高连通性保障的生物医学图像分割任务，如神经追踪
在生物医学图像中，如神经元过程和CT中的血管等曲线结构的语义分割，保持连通性仍是一大挑战。传统像素级损失函数（如交叉熵、Dice损失）难以捕捉高层次拓扑连通性，导致从预测图生成的图结构出现拓扑错误。本文提出一种新型损失函数——CAPE（Connectivity-Aware Path Enforcement），通过优化图连通性度量，强制分割图的连通性。CAPE利用真实标签的图表示，选取节点对，并通过最短路径算法确定其在预测分割图中的对应路径，从而惩罚断连和虚假连接，有效提升模型对拓扑正确性的保持能力。在2D与3D数据集（包括神经元与血管追踪）上的实验表明，CAPE显著提升了拓扑感知指标，优于当前最先进的方法。
原文摘要 · Abstract (English)
Promoting the connectivity of curvilinear structures, such as neuronal processes in biomedical scans and blood vessels in CT images, remains a key challenge in semantic segmentation. Traditional pixel-wise loss functions, including cross-entropy and Dice losses, often fail to capture high-level topological connectivity, resulting in topological mistakes in graphs obtained from prediction maps. In this paper, we propose CAPE (Connectivity-Aware Path Enforcement), a novel loss function designed to enforce connectivity in graphs obtained from segmentation maps by optimizing a graph connectivity metric. CAPE uses the graph representation of the ground truth to select node pairs and determine their corresponding paths within the predicted segmentation through a shortest-path algorithm. Using this, we penalize both disconnections and false positive connections, effectively promoting the model to preserve topological correctness. Experiments on 2D and 3D datasets, including neuron and blood vessel tracing demonstrate that CAPE significantly improves topology-aware metrics and outperforms state-of-the-art methods.
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