论文标题
在固有不确定性的背景下，软骰子优化分割图的体积偏差的理论分析和实验验证
Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty
论文作者
论文摘要
临床兴趣通常是测量通常从分割得出的结构的体积。为了评估和比较分割方法，使用流行的离散指标（例如骰子得分）来测量分割和预定义的地面真实之间的相似性。最近的分割方法使用可区分的替代度量，例如软骰子，作为学习阶段损失函数的一部分。在这项工作中，我们首先简要描述了如何从本质上不确定或模棱两可的分割中得出体积估计。接下来是理论分析和实验验证，将训练CNN的常见损失函数（即跨透明骰子和软骰子）联系起来。我们发现，即使软骰子优化导致骰子分数和其他措施的性能提高，但它可能引入固有不确定性高的任务的体积偏差。这些发现表明了该方法的一些临床局限性，并建议通过可选的重新校准步骤进行更紧密的临时体积分析。
The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using popular discrete metrics, such as the Dice score. Recent segmentation methods use a differentiable surrogate metric, such as soft Dice, as part of the loss function during the learning phase. In this work, we first briefly describe how to derive volume estimates from a segmentation that is, potentially, inherently uncertain or ambiguous. This is followed by a theoretical analysis and an experimental validation linking the inherent uncertainty to common loss functions for training CNNs, namely cross-entropy and soft Dice. We find that, even though soft Dice optimization leads to an improved performance with respect to the Dice score and other measures, it may introduce a volume bias for tasks with high inherent uncertainty. These findings indicate some of the method's clinical limitations and suggest doing a closer ad-hoc volume analysis with an optional re-calibration step.
