The soft Dice loss (SDL) has taken a pivotal role in many automated segmentation pipelines in the medical imaging community. Over the last years, some reasons behind its superior functioning have been uncovered and further optimizations have been explored. However, there is currently no implementation that supports its direct use in settings with soft labels. Hence, a synergy between the use of SDL and research leveraging the use of soft labels, also in the context of model calibration, is still missing. In this work, we introduce Dice semimetric losses (DMLs), which (i) are by design identical to SDL in a standard setting with hard labels, but (ii) can be used in settings with soft labels. Our experiments on the public QUBIQ, LiTS and KiTS benchmarks confirm the potential synergy of DMLs with soft labels (e.g. averaging, label smoothing, and knowledge distillation) over hard labels (e.g. majority voting and random selection). As a result, we obtain superior Dice scores and model calibration, which supports the wider adoption of DMLs in practice. Code is available at \href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.

翻译：软Dice损失（SDL）在医学影像社区的许多自动分割流程中扮演着关键角色。近年来，人们揭示了其优越性能背后的部分原因，并进一步探索了优化方法。然而，目前尚无支持在软标签场景中直接使用该损失的实现。因此，SDL的使用与软标签研究（包括模型校准相关研究）之间仍缺乏协同效应。本文提出Dice半度量损失（DMLs），其特点是：（i）在硬标签的标准设置下，设计与SDL完全一致；（ii）同时可在软标签场景中使用。我们在公共QUBIQ、LiTS和KiTS基准上的实验证实，相较于硬标签（如多数投票和随机选择），DMLs与软标签（如平均化、标签平滑和知识蒸馏）的协同潜力更为显著。最终，我们获得了更优的Dice分数和模型校准性能，这支持了DMLs在实际中的更广泛应用。代码见：\href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}。