骨架召回损失：用于薄管状结构连通性保持与资源高效分割
Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures
AI总结：
针对薄管状结构分割中连通性保持与计算开销问题，提出骨架召回损失，以CPU操作替代GPU密集计算，在五个数据集上性能优于现有方法，开销降低超90%，并支持多类分割。
摘要：
准确分割薄管状结构（如血管、神经、道路或混凝土裂缝）是计算机视觉中的一项关键任务。基于深度学习标准的分割损失函数（如Dice或交叉熵）侧重于体积重叠，往往以牺牲结构连通性或拓扑保持为代价。这可能导致分割错误，进而对下游任务（包括流量计算、导航和结构检查）产生不利影响。尽管当前关注拓扑的损失函数有所改进，但它们引入了显著的计算和内存开销。这对于3D数据尤其相关，使得这些损失函数在较大体积以及日益重要的多类分割问题上变得不可行。为缓解此问题，我们提出了一种新颖的骨架召回损失（Skeleton Recall Loss），通过使用廉价的CPU操作规避密集的基于GPU的计算，有效解决了这些挑战。它在五个公共数据集上展示了优于当前最先进方法的整体性能，用于拓扑保持分割，同时将计算开销大幅降低超过90%。由此，我们引入了首个支持多类的薄结构分割损失函数，在拓扑保持方面兼具高效性和有效性。
英文摘要：
Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision. Standard deep learning-based segmentation loss functions, such as Dice or Cross-Entropy, focus on volumetric overlap, often at the expense of preserving structural connectivity or topology. This can lead to segmentation errors that adversely affect downstream tasks, including flow calculation, navigation, and structural inspection. Although current topology-focused losses mark an improvement, they introduce significant computational and memory overheads. This is particularly relevant for 3D data, rendering these losses infeasible for larger volumes as well as increasingly important multi-class segmentation problems. To mitigate this, we propose a novel Skeleton Recall Loss, which effectively addresses these challenges by circumventing intensive GPU-based calculations with inexpensive CPU operations. It demonstrates overall superior performance to current state-of-the-art approaches on five public datasets for topology-preserving segmentation, while substantially reducing computational overheads by more than 90%. In doing so, we introduce the first multi-class capable loss function for thin structure segmentation, excelling in both efficiency and efficacy for topology-preservation.
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