检验了骨骼召回损失在细长结构分割中的有效性，发现其效果不如传统方法。
Does the Skeleton-Recall Loss Really Work?
- 通过梯度理论分析与实证对比，评估骨骼召回损失的性能
- 在多个管状结构数据集上，SRL模型未超越传统基线模型
- 适合关注拓扑损失局限性的计算机视觉研究者
图像分割是计算机视觉中一项重要且广泛应用的任务。在多样场景下实现有效分割通常需要定制模型架构和损失函数。针对细长管状结构分割的模型常采用基于拓扑保持的损失函数。这类模型通常利用像素骨架化过程，声称能生成更精确的细管分割掩码，并更好地捕捉其他模型易遗漏的结构。其中，Kirchhoff 等人提出的骨架召回损失（Skeleton Recall Loss, SRL）被宣称在基准管状数据集上达到顶尖性能。本文对SRL损失的梯度进行了理论分析，并在原始工作使用的部分数据集及额外数据集上对比了其表现，发现基于SRL的分割模型性能并未超过传统基线模型。通过提供理论解释与实证证据，本文批判性评估了基于拓扑的损失函数的局限性，为开发更有效的复杂管状结构分割模型提供了重要参考。
原文摘要 · Abstract (English)
Image segmentation is an important and widely performed task in computer vision. Accomplishing effective image segmentation in diverse settings often requires custom model architectures and loss functions. A set of models that specialize in segmenting thin tubular structures are topology preservation-based loss functions. These models often utilize a pixel skeletonization process claimed to generate more precise segmentation masks of thin tubes and better capture the structures that other models often miss. One such model, Skeleton Recall Loss (SRL) proposed by Kirchhoff et al.~\cite {kirchhoff2024srl}, was stated to produce state-of-the-art results on benchmark tubular datasets. In this work, we performed a theoretical analysis of the gradients for the SRL loss. Upon comparing the performance of the proposed method on some of the tubular datasets (used in the original work, along with some additional datasets), we found that the performance of SRL-based segmentation models did not exceed traditional baseline models. By providing both a theoretical explanation and empirical evidence, this work critically evaluates the limitations of topology-based loss functions, offering valuable insights for researchers aiming to develop more effective segmentation models for complex tubular structures.
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